master of science (geography)

Intra-storm attributes and climatology of extreme erosive events on

Mauritius, 2004 to 2008

by

Alexia Hauptfleisch

Submitted in partial fulfilment of the requirements for the degree

MASTER OF SCIENCE (GEOGRAPHY)

July 2016

© University of Pretoria

i

DECLARATION

I,_______________________________________, declare that this dissertation

entitled Intra-storm attributes and climatology of extreme erosive events

on Mauritius, 2004 to 2008, which is hereby submit for the degree Master of

Science (Geography) at the University of Pretoria, is my own work and has

not previously been submitted by me for a degree at this or any other tertiary

institution.

___________________________ ___________________________

SIGNATURE DATE


ii

Intra-storm attributes and climatology of extreme erosive events on

Mauritius, 2004 to 2008

Alexia Hauptfleisch

Promoters:

Professor Paul Sumner (University of Pretoria)

&

Professor Werner Nel (University of Fort Hare)

Department: Geography, Geoinformatics and Meteorology

Faculty: Natural and Agricultural Sciences

University: University of Pretoria

Degree: Master of Science (Geography)

Abstract

Topographic complexity and island-scale weather systems on Mauritius result in

highly variable spatial and temporal rainfall distribution. This, in combination with the intense

agricultural activity, predisposes the island to a high risk of rainfall induced soil erosion. Thus

it is important to investigate intra-storm attributes of erosive events, as these events are most

likely to cause significant degradation and reduced productivity of the soils on the island.

Intra-storm analysis allows for the identification of critical intensity peaks in rainfall events

that potentially impact the severity of erosion. Six Mauritius Meteorological Services

automated weather stations (measuring rainfall at 6 minute intervals) located on the west


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coast and in the interior providing rainfall data over a 5 year period (2004 to 2008), enabled

the first detailed intra-storm analysis on the island to occur. For the purpose of this study,

erosive events were defined as a total rainfall exceeding 12.5 mm and a maximum 6-minute

intensity exceeding 30 mm/h. The analysis found that there were 444 erosive events during

the study period which are responsible for generating the bulk of the rainfall erosivity. A total

of 120 erosive events (the top twenty erosive events for each weather station with the

highest ‘total kinetic energy generated’) were analysed to investigate the intra-storm

distribution of rainfall depth, extreme rainfall intensity and cumulative kinetic energy. General

climatological characteristics and weather circulation patterns were also determined.

Erosive events were found to vary both in rainfall depth and duration, but all the

stations indicate a clear exponential distribution of cumulative kinetic energy generated over

the duration of the rainfall events. Extreme rainfall intensities display noticeable temporal

differences between the stations in different climatic regions on the island. All the stations

received more than 80% of the potential kinetic energy content generated by the storms

within the first 2500 minutes of the storm, as well as 80% of the cumulative rainfall available.

Investigating the distribution of the extreme rainfall intensity (above 30 mm/h) as a function of

storm duration, reveals that 57% of the erosive events generate peak intensities within the

first half of the storm duration. Erosive events were not restricted to tropical cyclones, but

include other weather systems such as cold fronts.

The elevated centre of Mauritius, which is responsible for a high rainfall gradient

across the island, influenced the spatial and temporal variability in erosive events. Results

indicate that the intra-storm attributes of rainfall events are strongly dependent on the

geographic features within the immediate surroundings of the weather stations, and distance

between weather stations did not always lead to predictable differences in intra-storm

attributes. Although the erosive events on Mauritius share common characteristics, the

within-storm distribution shows that no two events are similar and no two stations show

comparable event pattern distributions. However, the intra-storm analysis of the erosive

events suggests that, despite the spatial differentiation in the structure and nature of the

erosive rainfall generalisations can be made regarding the erosion experienced in the coastal

and interior regions of the island. The inclusion of more automated weather stations is

warranted as this will provide a better representation of rainfall characteristics across the

remaining regions of the island. Further research is necessary to determine the relationship

between event structure and synoptic conditions experienced on the island.


iv

Acknowledgements

I would like to thank both my supervisors, Professor Paul Sumner, Department of

Geography, Geoinformatics and Meteorology, University of Pretoria (UP) and Professor

Werner Nel, Department of Geography and Environmental Sciences, University of Fort Hare,

for their professional guidance, support and enthusiasm towards my project. Your guidance,

insight and patience are much appreciated and I feel fortunate that you allowed me the

freedom to work at my own pace and explore my ideas. Thank you also for providing me the

remarkable opportunity of being able to travel to Mauritius through funding from the

University of Pretoria and University of Fort Hare.

The data I received from The Mauritius Meteorological Service (MMS) through my

supervisors is hugely appreciated. I would also like to thank Professor Ravindra Boojhawon

and Professor Soonil Rughooputh from the University of Mauritius for their assistance and

insightful knowledge of the island.

My sincerest thanks is also expressed to both my parents, Renee and George Hauptfleisch,

for their tireless love, emotional support and generous financial support throughout my study

career and allowing me the privilege of continuing with my studies. My parents have always

believed in me and pushed me to achieve more than I thought I could and without their

support and encouragement throughout my life I do not know where I would be today. I

would also like to thank my brother, my boyfriend and friends, especially Monica Leitner, for

all the support and keeping me sane throughout the duration of this project. Their support

and care helped me overcome setbacks and stay focused on my thesis. I greatly value their

friendship and I deeply appreciate their belief in me.

The academic support and friendship from my fellow colleagues and students in the

Department of Geography, Geoinformatics and Meteorology is also greatly appreciated. I

would also like to thank, Barend van der Merwe, Michael Loubser and Ryan Anderson for

their willingness to help, and always having an open door to chat.


v

Table of Contents

Abstract ..................................................................................................................... ii

Acknowledgements ................................................................................................. iv

List of Figures ......................................................................................................... vii

List of Tables ........................................................................................................... ix

Chapter 1 : Introduction ........................................................................................... 1

1.1. Rainfall induced soil erosion ....................................................................................... 2

1.2. Soil loss modelling ...................................................................................................... 4

1.3. Rainfall erosivity (R-factor) ......................................................................................... 8

1.4. The rainfall intensity and rainfall kinetic energy relationship ...................................... 12

1.5. The orographic effect on rainfall distribution and erosivity......................................... 15

1.6. Importance of ‘event profiles’ on infiltration, runoff and soil erosion .......................... 18

1.7. Aim and Objectives................................................................................................... 20

1.7.1. Aim .................................................................................................................... 20

1.7.2. Objectives .......................................................................................................... 20

1.8. Project Outline .......................................................................................................... 20

Chapter 2 : Study Area ........................................................................................... 21

2.1. Geology .................................................................................................................... 23

2.2. Topography and hydrology ....................................................................................... 24

2.3. Pedology (soils) ........................................................................................................ 28

2.4. Climate and weather ................................................................................................. 30

2.4.1. Weather systems in Mauritius ............................................................................ 31

2.4.2. Rainfall ............................................................................................................... 35

2.4.3. Climate classification.......................................................................................... 38

2.5. Land use and vegetation .......................................................................................... 39

Chapter 3 : Station Data and Methodology .......................................................... 42

3.1. Station data .............................................................................................................. 42

3.2. Research instruments ............................................................................................... 45

3.3. Missing, unreliable and incomplete data ................................................................... 46

3.4. Objective One: Identification of the top twenty events ............................................... 47


vi

3.4.1. Identifying an erosive event................................................................................ 47

3.4.2. Determining rainfall event kinetic energy ............................................................ 47

3.5. Objective Two: General rainfall, climatological characteristics and weather circulation

patterns associated with the top twenty erosive events ................................................... 49

3.6. Objective Three: Intra-storm analysis of each event ................................................. 50

3.6.1. Intra-storm distribution of rainfall depth .............................................................. 51

3.6.2. Intra-storm distribution of extreme and peak rainfall intensity ............................. 51

3.7. Objective Four: Contrast of spatial and temporal differences between the automated

weather stations .............................................................................................................. 52

Chapter 4 : Results ................................................................................................. 54

4.1. General storm and rainfall data and general climatological characteristics ............... 54

4.2. Intra-storm distribution of rainfall depth ..................................................................... 64

4.2.1. Storm rainfall depth and cumulative rainfall generated over time by the extreme

events .......................................................................................................................... 64

4.2.2. Storm rainfall depth as a function of storm duration ........................................... 69

4.3. Intra-storm distribution of extreme and peak intensity ............................................... 69

4.3.1. Timing of extreme (above 30mm/h) rainfall intensity generated by the erosive

events .......................................................................................................................... 69

4.3.2. Extreme rainfall intensity (above 30mm/h) as a function of storm duration ......... 72

4.4. Climatic drivers and temporal analysis of extreme events......................................... 75

Chapter 5 : Discussion........................................................................................... 79

5.1. Identification of the erosive events ............................................................................ 79

5.2. Spatial and temporal variations of the erosive events ............................................... 80

5.3. The influence of elevation on the erosive events at the respective stations .............. 82

5.4. The potential implication of the erosive events on soil erosion risk ........................... 85

Chapter 6 : Conclusion .......................................................................................... 89

6.1. Seasonality and spatial distribution of the erosive events ......................................... 89

6.2. Elevation and the erosive events .............................................................................. 90

6.3. Potential erosion risk of the erosive events ............................................................... 91

6.4. Research needs and recommendations ................................................................... 92

References: ............................................................................................................. 94


vii

List of Figures

Figure 2-1: Location map of Mauritius (after Saddul, 1995)………………………………..21

Figure 2-2: Locality and elevation map with the location of the capital Port Louis, SSR

International Airport and Curepipe, a major population centre (Anderson,

2012)……………………….…………………………….....................................22

Figure 2-3: Island profiles from SSW to NNE, and NNW to SSE (after Saddul, 1996;

taken from Le Roux, 2005)…………………………………………………….....25

Figure 2-4: Main geomorphological domains on Mauritius (after Saddul, 1995; taken from

Anderson, 2012)…..……………………………………………………………….26

Figure 2-5: Catchment boundaries, major rivers and water bodies on Mauritius

(WRU, 2007; taken from Anderson, 2012) ……………………………………..27

Figure 2-6: Classification of Soils in Mauritius (Proag, 1995)………………………………28

Figure 2-7: Soil map of Mauritius from DOS and MSIRI (1962), taken from Nigel

(2011)…………………………………………………………………………….....30

Figure 2-8: Trajectories of most severe tropical cyclones affecting Mauritius from 1892-

2005, with 2004-2008 cyclones presented in the text (MMS, 2014d)….…....33

Figure 2-9: Mean rainfall (1961 - 1990) and mean annual rainfall amount (Nigel,

2011)………………………………………………………………………………..36

Figure 2-10: The effect of relief on rainfall with annual rainfall along a 40km ESE-WNW

line at selected sites (Padya, 1989)……………………………………………..37

Figure 2-11: Landscape near the station at Albion (Photo taken 28 June 2010) ………...40

Figure 2-12: Landscape near Trou aux Cerfs (Photo taken 28 June 2010)………………..41

Figure 3-1: Location of automated weather stations………………………………………...43

Figure 3-2: Example of the automatic rainfall bucket used by the Mauritius Meteorological

Service (MMS) to record rainfall data. Also see Anderson (2012) and Mongwa

(2012)…………………………………………………………………………….....45

Figure 4-1: Mean rainfall depth and duration of the 120 erosive events analysed

in this study…………………………………………………………………………56

Figure 4-2: Cumulative kinetic energy generated over time by the erosive events at the

respective stations in Mauritius…………………………………………………..65

Figure 4-3: Rainfall depth and cumulative rainfall generated over time by the erosive

events measured at the respective stations on Mauritius: (A) Albion (B) Beaux

Songes……………………………………………………………………………...66


viii


events measured at the respective stations on Mauritius: (C) Arnaud (D)

Monbois…………………………………………………………………………….67


events measured at the respective stations on Mauritius: (E) Grand Bassin

(F) Trou aux Cerfs…………………………………………………………………68

Figure 4-6: Rainfall depth as a function of event duration at the respective stations in

Mauritius. (A) Albion; (B) Beaux Songes; (C) Arnaud; (D) Monbois; (E) Grand

Bassin; (F) Trou aux Cerfs………………………………………………………..70

Figure 4-7: Timing of extreme rainfall intensity (above 30mm/h) generated by the

erosive events at the respective stations (each individual event is presented

by a different symbol). (A) Albion; (B) Beaux Songes; (C) Arnaud; (D)

Monbois; (E) Grand Bassin; (F) Trou aux Cerfs……………………………….73

Figure 4-8: Timing of extreme rainfall intensity (above 30mm/h) as a function of rainfall

duration at the respective stations (each individual event is presented by a

different symbol). (A) Albion; (B) Beaux Songes; (C) Arnaud; (D) Monbois (E)

Grand Bassin; (F)Trou aux Cerfs………………………………………………...74


ix

List of Tables

Table 3-1: Location, altitude and climatic information of the automatic weather station

(also see Anderson, 2012; Mongwe, 2012; Nel et al.,

2012)………………………………………………………………………………..44

Table 3-2: Weather Systems in Mauritius (Fowdur et al., 2014) as representative

variables applied to the extreme events………………………………………...50

Table 4-1: Total intra-storm attributes at the six automated weather stations…………...56

Table 4-2: The top twenty erosive event attributes for Albion (12m.a.s.l) from

2004 to 2008……………………………………………………………………….58

Table 4-3: The top twenty erosive event attributes for Beaux Songes (225m.a.s.l)

from 2004 to 2008…………………………………………………………………59

Table 4-4: The top twenty erosive event attributes for Arnaud (576m.a.s.l) from 2004 to

2008…………………………………………………………………………………60

Table 4-5: The top twenty erosive event attributes for Monbois (590m.a.s.l)

from 2004 to 2008…………………………………………………………………61

Table 4-6: The top twenty erosive event attributes for Grand Bassin (605m.a.s.l)

from 2004 to 2008………………………………………………………...............62

Table 4-7: The top twenty erosive event attributes for Trou aux Cerfs

(614m.a.s.l) from 2004 to 2008…………………………………………………..63


1

Chapter 1 : Introduction

Mauritius is a Small Island Developing State (SIDS) in the Indian Ocean and is

particularly prone to erosion from intense rainfall events (Nel et al., 2012). On tropical islands

erosive rainfall is capable of detaching and transporting large amounts of sediments

(Calhoun & Fletcher, 1996) and is associated with rainfall amount, topography and altitude

(Nigel & Rughooputh, 2010a). Mauritius, like many tropical volcanic islands, has a distinct

elevated interior which acts as an orographic barrier, influencing the rainfall gradient across

the island. Different regions of the island consequently receive varying amounts of rainfall as

a result of island’s topographic features (Dhurmea et al., 2009). Soil erosion has long been

regarded as an important land denudation process on tropical islands (Cooley & Williams,

1985) and as a consequence of the climate, topography, altitude and the subsequent intense

rainfall, the interior of Mauritius is particularly prone to high levels of erosion (Nigel &

Rughooputh, 2010a; Nel et al., 2012).

Globally changing rainfall patterns are aggravating the risk of soil erosion and this is

most evident in counties with highly variable rainfall and strong erosive events (Sanchez-

Moreno et al., 2014). On tropical islands, the potential soil loss and erosion risk is not

necessarily only dependent upon the amount of rainfall received but rather the physical

characteristic of the event, such as rainfall duration, amount, drop-size distribution, terminal

velocity, wind speed and inclination (Nel et al., 2013). Rainfall patterns on Mauritius are

multi-faceted and complex due to the spatial variability of the topography. Peak rainfall

intensities, which influence infiltration and runoff rates, can therefore occur at any point

during the rainfall event. This is important factor to consider as rainfall event patterns (i.e.

when peak intensity occurs) have differing effects regarding the type of material eroded from

different soil types (Parsons & Stone, 2006). Therefore, recognising the intra-storm attributes

of erosive events is critical when attempting to understanding the rainfall erosivity and

potential soil erosion risks of the events.

The tropical island of Mauritius was selected for this project because it forms part of a

larger project on rainfall which stemmed from the earlier work by Le Roux (2005). Le Roux

(2005) modelled the potential soil loss in a southern catchment on Mauritius to investigate

the extent to which soil erosion is affected by different land use. The results showed that land

use change to pineapples and vegetables crops will have a drastic influence on soil erosion.

Upon conclusion of the study by Le Roux (2005), it was realised that there was not


2

sufficiently detailed rainfall data to accurately quantify erosivity. Therefore, the new high

resolution 6-minute rainfall data received from the Mauritius Meteorological Services afforded

the opportunity to conduct studies on storm kinetic energy, erosivity and soil erosion risk of

rainfall in Mauritius (Le Roux, 2005; Le Roux et al., 2005; Nigel & Rughooputh, 2010a, b; Nel

et al., 2012; 2013).

However, despite the aforementioned studies very little is known about intra-storm

rainfall attributes and the associated weather systems responsible for the distribution of

rainfall parameters. The purpose of this research is thus to investigate the intra-storm

attributes of the erosive events on Mauritius. This includes identifying and describing the

within-storm distribution of rainfall depth, extreme rainfall intensity, cumulative kinetic

energies and general climatological characteristics associated with these events and the

potential erosion risk for assisting conservation planning.

1.1. Rainfall induced soil erosion

Soil erosion is the detachment of individual particles from the soil surface by an

erosive agent and the subsequent transport of the soil particles to another location (Fornis et

al., 2005; Hoyos et al., 2005). Soil erosion is connected to a wider concept termed soil

degradation. The loss of topsoil is considered as only one of the major soil degradation

problems threatening agriculture throughout the world and involves physical, chemical and

biological deterioration (Dardis et al., 1988). Soil degradation includes loss of organic matter,

decline in soil fertility, the breakdown of soil structure and changes in salinity and acidity

(Haynes, 1997). The leading forces to soil degradation include deforestation, intense

cultivation and overgrazing of vulnerable land, pollution, as well as poor soil and water

management and all of them reduce the productive capacity of the soils (Le Roux, 2005).

The process of soil erosion is one accomplished through ‘work’, and is achieved in a

three-stage process that derives energy from numerous physical sources such as wind, ice

and water, gravity, chemical reactions and anthropogenic influences (Lal, 2001). It is the

source of energy such as snow, wind or water that controls the type of erosion process. The

rate and magnitude of energy providing such forces determines the severity of the erosion

process. The three stages of erosion are: (1) detachment of soil, (2) transport, and (3) the

deposition of soil (Lal, 2001). Water, predominately through rainfall, is believed to be the

foremost agent of soil erosion and includes processes such as runoff, rainsplash, rill and


3

gully development (Chapman, 2005; Hoyos et al., 2005). Other erosion agents such as wind,

ice and streams are referred to as aeolian erosion, glacial erosion and fluvial erosion

respectively (Morgan, 1995). Despite the common perception that soil erosion is a solely

natural process, human activities, such as land cover change and the disturbance of soil

structure through cultivation, can also cause and greatly aggravate natural process of

erosion (Yang et al., 2003).

Rainfall and its consecutive overland runoff is generally regarded a major driving

force of most hydrological and erosional processes through which soil particles are detached

and surface runoff is created (Moore, 1979; Nyssen et al., 2005; Nel, 2007). Bergsma et al.

(1996: 117) define rainfall erosion as: “The rate of soil loss expected in the near future, due

to rain erosion, depending on the combined and interactive effects of all erosion hazard

factors: climate, relief, soil profile, present erosion, land use and vegetation and cultivation

system.” Processes of soil erosion are highly dependent upon rainfall energy, which, in turn

relates to intensity and amount of rainfall as well as the size of the raindrops (Jayawardena &

Rezaur, 2000). The combination of rainfall intensity and raindrop fall velocity influences soil

splash rate (Nel, 2007), therefore, erosion is more intense when runoff and runoff velocity is

high (i.e. when hydraulic roughness is low) (Torri et al., 1999).

The soil erosion process is complex in nature as it results from interactions between

the soil itself, climate, relief, surface cover and land use practices (Hoyos et al., 2005). The

total soil loss for any time period is regarded as a function of two attributes: the resistance of

the surface cover and soil factors that change daily and the distribution of rainfall events for

the time period (Nearing et al., 1990). For example, heavy rain falling on bare soil might

cause more erosion than rain falling on well vegetated soil, hence the timing and intensity the

erosive rains with respect to soil cover is important (Moore, 1979). Therefore, soil erosion is

most accurately understood and predicted using knowledge of how the key soil and plant

parameters vary over time and how these changes influence soil erosion (Le Roux, 2005).

While some studies have shown that land cover serves as a protective layer between

the rainfall and the soil (e.g. Lal, 2001; Toy et al., 2002; Morgan, 2005; Nigel, 2011), land

cover can aggravate erosion depending on the above- and below-ground components

present at the land surface (Morgan, 2005; Nigel, 2011). Above-ground components of

vegetation (leaves and stems) intercept falling raindrops and running water, therefore

reducing the amount of energy available to detach soil particles (Morgan, 2005). Below-

ground vegetative components (roots) enhance the mechanical strength of the soils and offer


4

resistance against the detachment and transportation by surface runoff (Morgan, 2005).

Different types of vegetation cover provide differing degrees of protection and thus, human

influence on the land use can alter the rate of erosion to a large extent (Nigel, 2011).

The commencement of agricultural practices led to soil erosion becoming a serious

problem globally (Renschler et al., 1999) by causing a reduction in arable land, increased

landslide activity and contaminant diffusion by the inflow of sediment to river and ecosystem

disturbance (Lee & Heo, 2011). Environmental problems caused by soil erosion exacerbate

on-site land degradation as well as increase the sediments and pollutants that adversely

affect off-site aquatic ecosystems. Therefore, it is essential to implement conservation

measures which successfully reduce the impact of both on- and off-site effects of soil erosion

(Nigel & Rughooputh, 2010b). Lal (2001) emphasised that erosion cannot necessarily be

prevented but the adoption of soil conservation measures can allow the effects to be reduced

to an appropriate level. Hence, the universal aim of soil conservation is a reduction in erosion

levels that allow for a sustainable level of agricultural production and grazing (Morgan, 2005).

On Mauritius the hydrological and erosional process at a regional scale are controlled

by soil type, climate patterns in relation to topography and land use (Nigel & Rughooputh,

2010b). As a result of the rainfall variability and characteristics Mauritius is particularly

sensitive to erosion risks (Nigel & Rughooputh, 2010a). The island’s dependence on its

agricultural sector renders it vital to estimate and evaluate the amount of soil erosion through

soil loss modelling to allow for effective soil conservation, disaster control and water

management (Lee & Heo, 2011).

1.2. Soil loss modelling

The estimation and quantification of soil loss has been the subject of studies since

the 1940’s (Wischmeier & Smith, 1978; Lee & Heo, 2011). Numerous models have

subsequently been developed and placed in practise in an effort to quantify soil loss. These

include the European Soil Erosion model (EUROSEM) (Morgan et al., 1998), Water Erosion

Prediction Project (WEPP) (Flanagan & Nearing, 1995), Mediterranean Rainfall Erosivity

Model (MEDrem) (Diodato & Bellocchi, 2010), the Soil Loss Estimation Model for Southern

Africa (SLEMSA) (Elwell, 1976) and in the Mauritian context, the Mauritius Soil Erosion Risk

Mapping (MauSERM) (Nigel & Rughooputh, 2010a). Each of these models considers a

variety of factors and have their own limitations and/or advantages.


5

The Universal Soil Loss Equation (Wischmeier & Smith, 1978) as well as its reviewed

format, the Revised Universal Soil Loss Equation (Renard et al., 1997), is one of the most

widely used models in quantifying soil loss. The Universal Soil Loss Equation (USLE) is

considered as the first attempt in evaluating and qualifying human’s impact on soil erosion

through land use changes or using new cultivation techniques (Renschler et al., 1999). It was

initially developed between 1940 and 1956 to quantify soil loss in the ‘corn belt’, situated in

the Midwestern region of the United States of America, where corn is the primary crop of

cultivation. After being altered on numerous occasions, the formula was published in its

existing arrangement in the National Runoff and Soil Loss Data Centre under the title

‘Predicting rainfall erosion losses- A guide to conservation planning’ (Wischmeier & Smith,

1978). Subsequently, all factors of the original formula were re-examined, modified and

improved resulting in the creation of the Revised Universal Soil Loss Equation (RUSLE)

(Renard & Freimund, 1994) which has been extensively as tools in the prediction of soil

erosion in many parts of the world (Renschler et al., 1999). For example, it has been used by

Kremer (2000); Le Roux (2005); Seeruttun et al. (2007) and Nigel (2011) in Mauritius;

Onyando et al. (2005) in Kenya, Irvem et al. (2007) and Erdogan et al. (2007) in Turkey and

Schiettecatte et al. (2008) in Cuba.

(R)USLE erosion model was designed to predict the long-term average annual soil

loss carried by runoff from field slopes in specified cropping and management systems

including rangelands (Renard & Freimund, 1994; Wang et al., 2002). Thus the (R)USLE

equation quantifies soils erosion as a product of six factors representing rainfall and runoff

erosivity in (R), soil erodibility (K), slope length (L), slope steepness (S), crop type and

management practices (C), and supporting conservation practices (P). The equation is

(Renard & Freimund, 1994):

A = R x K x L x S x C x P

where A is the rate of soil loss per unit of area (t ha-1 year-1) and is expressed in the

units selected for K and the period selected for R. The rainfall and runoff erosivity factor (R)

and soil erodibility factor (K) are the only two factors in the equation with definable units

(Renard & Freimund, 1994). Rainfall and runoff erosivity (R) will be discussed in more detail

as it is acknowledged as one of the best parameters for predicting the erosive potential of

raindrop impact, and in turn of the potential of transport capabilities of runoff generated by

erosive storms (De Santos Loureiro & De Azevedo Coutinho, 2001).

The use of the (R)USLE model is advantageous because it has been widely applied

and tested over many years due to this its validity and well-known limitations (Renard et al.,


6

1997). These limitations are as a result of being developed from data limited to the Midwest

of the United States of America. This subsequently necessitated major adjustments to the

key factors of the equation in order for the algorithms of the models to be applicable in other

areas (Shamshad et al., 2008). Renard & Freimund (1994) acknowledge that (R)USLE are

empirical relationships that are only deemed valid within the range of experimental conditions

from which they were derived. However, since (R)USLE represents the major factors

affecting erosion, transferring it to locations throughout the world only requires the

determination of appropriate values for the different factors for the region in question (Renard

& Freimund, 1994). Thus, the use of (R)USLE is applicable in the Mauritian context.

Mauritius’ topography, where nearly 31% of the island has slopes >8%, creates areas

that are considerably more susceptible to experiencing potentially severe erosion risk (Nigel,

2011). In spite of this only a few erosion risk assessments have been completed probably

due to the absence of local legislation concerning soil erosion research, control and

conservation and the lack of assessment methodologies (Nigel & Rughooputh, 2010a).

Advantages of soil erosion risk mapping include its ability to depict the temporal variations in

erosion patterns, to define and priorities focal areas for conservation measures and the

promotion of better land use management, agricultural practices and conservation planning

(Nigel & Rughooputh, 2010b). The predominant focus of soil erosion risk mapping on

Mauritius is on cultivated lands, more specifically sugarcane canopy cover.

The most noteworthy of the soil assessment studies on Mauritius include: Kremer,

(2000); Le Roux et al., (2005); Seeruttun et al., (2007) and Nigel & Rughooputh, (2010a, b).

Kremer (2000) mapped erosion risk for the island by utilising three scenarios of sugarcane

canopy cover, those of 0-10, 30 and >70%, using land cover, slope, soil and rainfall data.

Kremer’s (2000) results indicated that in general erosion risk is low for canopy cover of >70%

and vice versa (Nigel & Rughooputh, 2010a). Le Roux et al. (2005) used the (R)USLE and

SLEMSA models within a GIS environment to estimate the soil loss for a river basin with

steep slopes. To obtain the rainfall erosivity (R value), Le Roux (2005) utilised monthly and

annual rainfall as inputs into a Modified Fournier Index (MFI) developed by the FAO

(Arnoldus, 1980) and concluded that erosion rates are generally highest on steep slopes

(>20%) in areas with a high annual rainfall (2400mm). Results also predicted that soil loss

has a strong inverse relationship with vegetation cover, where infrequently disturbed land

use type such as natural vegetation, tea and banana plantations experience low soil loss

values (1 to 4 t.ha-1.yr-1). In contrast, the frequently disturbed land use types such as

intercropped cane and vegetables experience moderate (13 t.ha-1.yr-1) to very high (80 t.ha-

1.yr-1) soil loss rates (Le Roux, 2005). The study, which was done within the context of


7

changing land use, confirms that soils erosion within sugarcane is less than that modelled

under other crop types. Findings show that the most vulnerable time for erosion is in the

early part of the wet season when there is high rainfall but the vegetation has not fully

developed (Le Roux et al., 2005). Hence, certain crops should be confined to low slope angle

and be supported by soil management practices (Le Roux et al., 2005). The SLEMSA model

provided much higher values of soil loss than the (R)USLE model, due to the high sensitivity

that SLEMSA had to rainfall energy (Le Roux, 2005; Anderson, 2012).

In another soil risk assessment study, Seeruttun et al. (2007) attempted to measure

soil loss using five field sites over four consecutive years in Mauritius. Each site had two

plots, one bare and one planted with sugarcane, in order to quantify the impact which

sugarcane has on soil loss in Mauritius. The study found that soil loss on bare plots ranges

between 0.5-37.6 ha-1.yr-1 depending on the soil type and in general, sugarcane reduced soil

loss by 80-99%. Seeruttun et al. (2007) also found that between 48% and 68% of the soil

erosion was associated with cyclonic activity. This study was, however, conducted only on

plots containing linear slopes <9% isolated from upslope contributing areas (Nigel &

Rughooputh, 2010b).

More recent soil erosion risk assessments on Mauritius (Nigel & Rughooputh, 2010a;

2010b) have indicated that soil erosion on the island is a problem. Nigel and Rughooputh

(2010a; b) developed and utilised the Mauritius Soil Erosion Risk Mapping (MauSERM)

model to investigate the variability of erosion patterns on Mauritius to establish conservation

efforts for the high erosion risk sites and priority action areas. Over half of the area of

Mauritius is under intensive cultivation, typically sugarcane plantations, which were found to

experience moderate sheet erosion (Atawoo & Heerasing, 1997). The rugged topography,

intensive cultivation (predominantly sugarcane) and its tropical climate are the main factors

responsible for making Mauritius vulnerable to soil erosion (Nigel & Rughooputh, 2010a).

Mean monthly rainfall and annual data were used as inputs into the Modified Fournier

Index (MFI) to determine the erosivity of the island (Nigel & Rughooputh, 2010a). In this case

the MFI provides better correlation with the observed erosivity patterns (Nigel & Rughooputh,

2010b). According to Nigel & Rughooputh (2010a), January and February have the highest

erosion risk as a result of torrential rains endured during these months, followed by

December and March, with the remainder of the year being dominated by low to very low

erosion risk. The western portion of the island had low erosivity, which is in accordance with

the low annual rainfall that the region observed. The central and eastern parts of the island

sustain the highest erosivity on an annual basis, with the majority of the erosivity experienced

during the wet season (Anderson, 2012). During the months of high erosivity, sugarcane


8

cultivation found on the steep slopes does not offer much protection against soil erosion from

rainfall (Mongwa, 2012).

The studies mentioned above utilised the Modified Fournier Index (MFI) when

calculation rainfall erosivity used in the soil erosion calculations, which is dependent on

monthly and annual totals (Anderson, 2012). The main reason for using total rainfall in

erosion risk assessment is the lack of high resolution data on an island scale (Mongwa,

2012). However, despite numerous studies that have been done on soil erosion risk mapping

(Le Roux, 2005; Nigel & Rughooputh, 2010a; b) and erosivity (Nel et al., 2012; 2013) on the

island, very little is known about the intra-storm attributes of the rainfall. No other study has

identified the erosive rainfall event characteristics and intra-storm attributes from the high

resolution rainfall data available. As the implication of the erosive events on the potential

erosion risk on the tropical island of Mauritius is unknown, a comprehensive intra-storm

analysis at an event scale is required to understand the impact that intra-storm dynamics

have on potential soil erosion risk.

1.3. Rainfall erosivity (R-factor)

Rainfall is regarded as a crucial factor for erosion processes (Petan et al., 2010) as it

can potentially detach and transport soil particles through the impact of raindrops striking the

soil surface and/or surface runoff generated during a storm (Lal & Elliot 1994; Le Roux,

2005). Rainfall owing to its erosivity is the agent for soil erosion as a result rainfall with higher

erosivity causes higher erosion (Nigel, 2011). Rainfall erosivity, unlike some other natural

factors such as relief or soil characteristics, is not responsive to human modification.

Consequently, it represents a natural environmental constraint that limits and conditions land

use and management (Angulo-Martínez & Beguería, 2009). Rainfall erosivity is defined as

the ability of rainfall and runoff to cause erosion through the detachment and transportation

of soil particles (Lal & Elliot, 1994; Obi & Salako, 1995). It is determined mainly through

rainfall kinetic energy, a parameter easily related to the rate or total amount (energy) of

rainfall, and other physical rainfall characteristics such as duration, intensity, drop-size

distribution, terminal velocity and extraneous factors such as wind velocity and slope angle

(Obi & Salako, 1995; Nyssen et al., 2005).

Erosivity is generally expressed in terms of rainfall amount and intensity since both

determine the potential of rainfall event to be erosive (Hoyos et al., 2005). Rainfall kinetic

energy is considered the most suitable expression of rain erosivity as it influences sediment


9

transportation and acts as a major factor in initiating soil detachment (Lal & Elliot, 1994;

Morgan, 1995). The quantification of rainfall erosivity, through the kinetic energy of a rainfall

event, is therefore central to the assessment of soil erosion risk. It is a commonly held theory

that a large proportion of soil erosion and sediment delivery takes place during several

intense rainfall events (of high kinetic energy) which produce the bulk of soil loss from an

island (Rydgren, 1996; Angulo-Martínez & Beguería, 2009). Alternatively, the cumulative

influence of more frequent yet lower magnitude events (of lower kinetic energy) might

overshadow the effect of the less frequent high-intensity events (Boardman & Favis-Mortlock,

1993; Trustrum et al., 1999). Page et al. (1994) argue that while rainfall event magnitude and

frequency can be analysed from rainfall records, the cumulative effects of rainfall-induced

erosion are more difficult to quantify, particularly in tropical areas where rain events tend to

be more intense (Nyssen et al., 2005). Intense rainfall in tropical areas, such as Mauritius,

has a particularly high erosive potential, and is influenced by the event type and rainfall

characteristics which might vary with altitude and topography (Nel et al., 2013). The temporal

and spatial variability of rainfall erosivity is therefore important to consider in tropical regions,

especially due to the existence and interactions of numerous factors, such as the Inter

Tropical Convergence Zone (ITCZ), topography (hills) and the ocean, on rainfall variability

(Salako, 2008).

Rainfall erosivity can also be dependent on vegetation cover and how it varies over

seasons (Jackson, 1972; Moore, 1979). For example, if heavy rainfall events occur at the

onset of the wet season when the vegetation cover is sparse and the soil is not well

protected, considerably higher levels of runoff and erosion are experienced. Towards the end

of the rainy season, vegetation cover provides more protection to the soil, resulting in

potentially less runoff and rainsplash occurring. In Mauritius, the soil is considered most

vulnerable during the early part of the wet season when the rainfall is at its highest, but the

vegetation growth does not yet provide sufficient protection to the soil (Nigel & Rughooputh,

2010b). Therefore, rainfall intensities, storm duration, frequency and seasonal occurrence of

rainfall all influence the amount of rainfall erosion potentially experienced by an area

(Jackson, 1972).

An interaction also prevails between vegetation cover and slope in influencing the

magnitude of rainfall erosion experienced by an area. Even though the influence of slope on

soil loss is secondary to that of vegetation cover, Snyman (1999) highlights that as

vegetation basal cover lessens, during dry periods or overgrazing, the influence of slope on

rainfall erosion increases. Long steep slopes such as those found in the upper catchment

and mountainous areas of Mauritius thus render the land extremely susceptible to erosion


10

once the vegetation cover is degraded (Le Roux, 2005). Furthermore, slope steepness can

also be increased by the effect of other erosion factors, such as raindrop impact leading to a

possible decrease in infiltration, more overland flow and further erosion (Smith et al., 2000).

Total soil loss cannot, however, be explained purely by the variation in slope length, slope

angle and vegetation cover because of the existence of complex interrelationships between

the microtopography, rainfall energy, plant cover and soil properties (Le Roux, 2005).

Rainfall erosivity is one of the six factors in the Universal Soil Loss Equation (USLE)

(Wischmeier & Smith, 1978), as well as the Revised Universal Soil Loss Equation (RUSLE)

(Renard et al., 1997) for erosion prediction, and is the most precisely defined factor of both

these equations (Yu et al., 1998). Erosive power of precipitation is accounted for by a rainfall-

runoff erosivity factor R, therefore combining the effects of the magnitude, duration and

intensity of each rainfall event (Bonilla & Vidal, 2011). Rainfall erosivity is both a numerical

description and quantifier of the potential of rainfall to cause soil loss at a hillslope scale (Yin

et al., 2007; Lee & Heo, 2011). It is commonly accepted that rainfall and runoff lead to soil

loss and subsequently, if all other parameters of the formula are kept constant, soil loss is

directly proportional to the rainfall erosivity (Wang et al., 2002). According to van Dijk et al.

(2002) the amount of soil detached by a particular depth of rain is related to the intensity at

which the rain falls. Rainfall erosivity (R or R-factor) is regarded as a dependent of splash

detachment and is reliant on the kinetic energy (KE) of the rainfall which varies with drop

mass as well as rainfall intensity (Brooks & Spencer, 1995).

In rainfall erosivity studies, the Rainfall erosivity (R or R-factor) is derived by

calculating the mean annual sum of individual erosive event erosion index values (EI30). This

is determined by the value of the total kinetic energy (KE) and maximum 30 minute rainfall

intensity (I30) (Renard et al., 1997; Shamshad et al., 2008; Lee & Heo, 2011). The EI30 term is

the abbreviation for the (KE) multiplied by I30 (Renard & Freimund, 1994) and was found to

produce the best correlation with soil loss from a rainfall event (Wischmeier & Smith, 1958).

EI30 calculations make use of breakpoint rainfall intensity data derived from automated

rainfall gauges. These data are often manually read from graphical charts from continuously

recording rain gauges, which record pairs of values representing time and cumulative depth

of rainfall as measured from the charts. Time intervals between these recorded pairs is

assumed to represent portions of the storms that demonstrate constant or near constant

rainfall intensities (Yin et al., 2007). As a result, the recorded points indicate times of

apparent “breaks” or variations in the rainfall intensity of the storm (Yin et al., 2007). As

breakpoint data are rarely available, EI30 is often calculated by using fixed interval rainfall

data from yearly, monthly and daily rainfall data. Breakpoint rainfall information is, however,


11

becoming more widely available with the development of automatic weather stations (Yin et

al., 2007). Rainfall data at automatic weather stations that is recorded in fixed time intervals,

such as 60 minutes, 15 minutes and even higher time resolution provides the ideal

substitution of breakpoint records. Furthermore, the use of high resolution rainfall data is

encouraged to determine the maximum 30 minute rainfall intensities for individual storms and

heavy storm events (Lee & Heo, 2011), as such information will allow for the thorough

evaluation of rainfall erosivity (Bonilla & Vidal, 2011).

Accurate calculation of each storm’s rainfall erosivity requires high resolution rainfall

data measurements (Wischmeier & Smith, 1978; De Santos Loureiro & De Azevedo

Coutinho, 2001). However, these data are not always readily available to calculate the R

value (De Santos Loureiro & De Azevedo Coutinho, 2001). Several other methods were

established to calculate the rainfall erosivity index (R). Examples include the Fournier Index

(Fournier, 1960), Morais et al. (1991) modified form of the Fournier index (known as the

MMFI), the Grimm-Jones-Rusco-Montanarella (GJRM) model (2003) and the Diodato-

Bellocchi Rainfall-Erosivity Model (REMDB) (2007) (Diodato & Bellocchi, 2007). Another

approach is to integrate climatic and geographical characteristics, which are not difficult to

record and capture variability at both a regional and sub-regional scale, for example monthly

average rainfall and geographical characteristics that can be incorporated into an erosivity

model (Diodato & Bellocchi, 2007). In a tropical island context, most rainfall erosivity is

calculated through annual and monthly rainfall depth in the absence of detailed intensity data

(see Nel et al., 2013).

The Fournier Index was designed for the west coast of Africa (Fournier, 1960) and

has been widely used where only monthly data are available. The Modified Fournier Index

(MFI) was developed by Arnoldus (1980) after correlating the Fournier index with R in

Morocco and finding poor correlations in general, but higher (R) values when utilising the

monthly average as an alternative to the maximum monthly rainfall (Sanchez–Moreno et al.,

2014). It was suggested by Renard & Freimund (1994) that the Modified Fournier Index be

used in areas where long-term data are not available or in the absence of reliable EI30

calculations. Consequently, the Modified Fournier Index has been used to map erosivity and

parameterise soil erosion risk as either a single factor, or correlated with R, in several

countries, including Spain (Angulo-Martínez & Beguería, 2009) and Mauritius (Le Roux,

2005; Nigel & Rughooputh, 2010a; b). In the Mauritian context, Nel et al. (2013) argue that

despite coarser time intervals producing less accurate results, the MFI still remain useful in

determining the relative spatial relationships of erosivity.


12

Rainfall erosivity appears to be higher in tropical regions compared to that of

temperate regions of the world (Salako et al., 1995; Lal, 1998; Nyssen et al., 2005;

Anderson, 2012) as tropical rains are considered more intense and are more concentrated in

time than other climates (Lal, 1998; Nyssen et al., 2005). As tropical areas experiences

higher amounts and frequency of intense erosive events, the average EI30 values in tropical

areas are generally higher than those in temperate regions (Hoyos et al., 2005).

Subsequently, rainfall induced soil erosion particularly effects tropical island environments,

such as Mauritius, due to the intense rainfall experienced there (Hoyos et al., 2005). It has

been noted that the erosive nature of rainfall on volcanic islands is closely related to rainfall

depth and intensity, such that a few extreme rainfall events with high rainfall intensity can

generate the bulk of the cumulative erosivity, as opposed to frequent events of low intensity

(Nel et al., 2013). It is therefore imperative to understand and quantify rainfall erosivity in

these environments as this process plays an important role in shaping the island’s

landscape.

1.4. The rainfall intensity and rainfall kinetic energy relationship

Rainfall kinetic energy (KE) results from the kinetic energy of each individual raindrop

striking the ground and represents the total energy available for the detachment and

transportation of soil particles (Salles et al., 2002; van Dijk et al., 2002; Salako, 2007).

Rainfall kinetic energy is expressed as either rainfall energy expended per volume of rain or

kinetic energy rate. The KE (the product of mass and fall velocity squared) of raindrops

weakens the bonding effects within the soil and provides the energy required to transport the

detached particles away from the site of impact (Wang et al., 2014). As rainfall intensity

contributes to runoff and sediment generation, the amount of soil detached by a particular

amount of rain is related to the intensity because rainfall striking the surface could potentially

detach more soil particles when falling at higher intensity (van Dijk et al., 2002; van Dijk et

al., 2005; Wang et al., 2014). Wang et al. (2014) expresses that though the role of rainfall

intensity could be considered as ambiguous when the infiltration capacity of the soil is

surpassed during short-duration, high-intensity storms likewise during long-duration, low-

intensity storms, both of which may possibly lead to the onset of erosion. Given the right

conditions, higher rainfall intensities could cause higher infiltration excess runoff rates thus

retaining more sediment in transport as well as actively entraining soil particles (van Dijk et

al., 2005). The usage of energy parameters, such as rainfall intensity and rainfall kinetic


13

energy indirectly, are generally accepted as better predictors of rainfall erosivity over a wide

range of conditions (Stocking & Elwell, 1976; Wang et al., 2014).

As rainfall kinetic energy (KE) is regarded as an indicator of rainfall erosivity (van Dijk

et al., 2002) many empirical and process-based soil erosion models make use of KE as the

rain erosivity index (Salles et al., 2002; Fornis et al., 2005). For example, rainfall kinetic

energy (KE) is used in splash erosion modelling such as used by Poesen (1985) and in

modelling sheet and rill erosion, such as SLEMSA (Elwell, 1976), in EUROSEM (Morgan et

al., 1998) or in RUSLE (Renard et al., 1997) as an indicator of rainfall erosivity (Salles et al.,

2002). There are two formulas of rainfall kinetic energy which can be used in relation to

rainfall intensity. One is kinetic energy per unit time that is often called the rate of kinetic

energy expenditure and designated as KER (J m-2 h-1). The other form is the kinetic energy

per unit area per unit depth also termed kinetic energy content and designated as KE (J m -2

mm-1) (Fornis et al., 2005). Hence, kinetic energy content is one of the many variants of

kinetic energy. Kinetic energy content is the volume-specific KE or kinetic energy ‘content’ (J

m-2 mm-1) encountered by 1m2 surface area per unit depth of rainfall. Other published

symbolisations for this variant are KEB, EB and KEmm (Brodie & Rosewell, 2007).

Fornis et al. (2005) established that there are three mathematical models which are

most commonly used to relate kinetic energy content to rainfall intensity, namely the

exponential model, the Hudson (1965) model, and the logarithmic model. The respective

forms of these models are presented as follows (Fornis et al., 2005):

The logarithmic model:

KE= u + w log I

The Hudson (1965) model:

KE= b – cI-1

The exponential model:

KE= z[1 – p exp(-hI)]

Where u, w, b, c, z, p, and h are empirical constraints.

As the exponential model has one parameter more than the logarithmic model it

offers more adaptability in tailoring the model to data sets (Fornis et al., 2005). Kinnell (1981)

suggests that the exponential model describes the KE – I relationship better than the

logarithmic form because the exponential model indicates that there is an upper limit to the


14

kinetic energy content of rainfall. The inclusion of a threshold value, or upper limit, for kinetic

energy reduces the overestimation of the rainfall erosivity of low intensity rainfall events and

thereby facilitates the calculation of gross erosion as well as accounting for differences in

rainfall characteristics inherent to the geographic location of the measuring site (van Dijk et

al., 2002). The exponential model allows both forms of KE to be determined precisely from

any rainfall event using the KE – I relationship because rain intensity data is widely available

and straight-forward to obtain in comparison to KE (Salles et al., 2002). The exponential

equation is commonly utilised in tropical areas (van Dijk et al., 2002).

Although van Dijk et al. (2002) cites a study by Rose (1960) who originally concluded

that rainfall momentum is marginally better as a predictor of soil detachment than kinetic

energy, it was demonstrated in a study by Hudson (1971) that momentum and kinetic energy

display favourable comparable relationships with intensity for natural rainfall. While it is

largely accepted that insight on the kinetic energy of rainfall is important in soil erosion

studies, its computation by direct measurements is not as common as the measurement of

intensity (Fornis et al., 2005). Direct measurements of rainfall kinetic energy are very

uncommon because they require both sophisticated and costly instruments (Petan et al.,

2010). Thus, the alternative approach is to estimate kinetic energy from rainfall intensity as it

can be more conveniently measured and is commonly available in most countries (Fornis et

al., 2005).

Rainfall kinetic energy is thus often derived through rainfall intensity (I) data which are

widely available by implementing the empirical kinetic energy–intensity (KE – I) relationship

(Petan et al., 2010). The KE – I relationship was developed by Wischmeier & Smith (1958)

as a linear-log equation and has subsequently inspired many later works (Petan et al., 2010).

The empirical kinetic energy–intensity (KE – I) relationship was established from rainfall

kinetic energy and has been formulated from raindrop size distribution (DSD) measurements

performed at certain locations with specific climatic conditions (Petan et al., 2010).

Information provided by the drop-size distribution (DSD) measurements in conjunction with

the fall velocity measurements or the empirical laws that link terminal fall velocity (Vt) and

drop diameter (D), allows for the calculation of rain kinetic energy (Salles et al., 2002). The

empirical relationship is effective for a limited range of rainfall intensity, hence before making

use of any KE – I relationship in climatically different environment, one should justify its

formulation before its implementation (Petan et al., 2010).

As rainfall kinetic energy represents the total energy available for detachment and

transport by rainsplash, the empirical relationship between rainfall intensity and kinetic

energy is vital for the prediction of erosion hazards (van Dijk et al., 2002). Data on the KE of


15

rainstorms are thus essential in developing and verifying models of soil detachment by

raindrop impact on interrill areas (Wang et al., 2014). Relating KE to easily measured rainfall

parameters would therefore be a more practical and convenient way to estimate the

erosiveness of rainstorms (Wang et al., 2014). Rainfall events with intensities greater than

25mm/h are more likely to be erosive and the amount of rain falling at higher intensities may

also be important in contributing to higher levels of soil erosion (Elwell & Stocking, 1973;

Moore, 1979). Moore (1979) found that in tropical regions, which frequently experience high

intensity rainfall events, as kinetic energy increases so does rainfall intensity up to about

75mm/h and believes that this relationship could underestimate the kinetic energy of tropical

storms by as much as 10 per cent (Salako, 2007). The effect of events with different rainfall

intensities on soil erosion risk in sugarcane fields was studied by the Mauritius Sugar

Industry Research Institute (MSIRI) (Le Roux, 2005; Nigel, 2011). On bare sugarcane

interrows the soil loss rate averaged low values between 0.2 and 5 t.ha -1.yr-1 at a rainfall

intensity of 90mm.h-1. According to the MSIRI study a threshold rainfall of about 60mm-1 is

present above which erosion starts to occur. While the MSIRI research provides valuable

information, it was only conducted on a few sugarcane fields on two soils types (Low Humic

Latosols and Dark Magnesium Clay) with slopes ranging from 7 to 13% and therefore it has

limited application to other parts of the topographically complex island (Le Roux, 2005).

1.5. The orographic effect on rainfall distribution and erosivity

The classical depiction of orographic precipitation is a mountain range in the

midlatitudes whose axis lies perpendicular to the prevailing wind direction (Roe, 2005).

Orography is the influence of mountain topography on subaerial weather conditions, creating

spatial and temporal variation of rainfall (Terry & Wotling, 2011). It is well established that

topography plays a major role in the development of cloud and rainfall through orographic

lifting (Terry & Wotling, 2011; Tobin et al., 2011). The best known relationship of the

orographic effect is rainfall increasing with elevation (Prudhomme & Reed, 1998). Orography

occurs when air lifts as it flows over mountains triggers convective instability and enhances

the chance of rainfall, consequently more precipitation falls on the windward compared to

leeward slopes (Terry & Wotling, 2011). Accordingly, in the leeward side of major mountain

barriers that occupy latitudes with consistent prevailing winds, the familiar ‘rain-shadow’

effect is observed (Terry & Wotling, 2011).


16

In regions characterised by complex orography and precipitation gradients, such as

often found on volcanic islands, topographic forms can trigger rain storms by extracting

atmospheric moisture through orographic precipitation mechanisms (Johansson & Chen,

2003; Boni et al., 2004). Orographic precipitation is particularly evident on mountainous

islands, such as Mauritius, where maritime winds in predominant direction and high-elevation

topography cause adiabatic cooling through forced uplift. This creates a greater possibility for

rain production by producing a cloudy environment that reduces evapotranspiration losses

(Custrodio et al., 1991; Terry & Wotling, 2011). Depending on the size of the topographical

feature and efficiency of the rainfall release processes the windward side will experience an

increased in precipitation because the forced lifting of the approaching air masses causes

the discharge of rainfall and a potential increase in precipitation with elevation (Johansson &

Chen, 2003). Accompanying this is the ‘rain shadow’ effect on the leeward side (Nel et al.,

2012). A proportion of the heavy rainfall experienced on Mauritius appears to be induced by

the elevated central plateau (Nel et al., 2012). Such orographic influences on the spatial and

temporal distribution of rainfall and moisture gradient also have important implications for

water resource availability and provision (Terry & Wotling, 2011). In places where a major

topographic barrier interacts intensely with narrow zones of mesoscale convective systems

then unusually heavy rainfall and the generation of significant flood events may occur (Terry

& Wotling, 2011).

Topography, from a small hill to a large-scale mountain range, influences all scales of

atmospheric motion (Bender et al., 1985). In the case of a landfalling tropical cyclone, the

topography of the specific terrain being transversed has a vital impact on certain aspects of

the rainfall event’s behaviour such as the event’s movement, rate of decay and rainfall

distribution. Strong relationships have been proven between the total rainfall associated with

landfalling tropical cyclones and the local distributions of orography. Therefore the maximum

hourly rainfall is generally higher in the mountain upslope regions (Bender et al., 1985).

On a global scale numerous studies have been conducted in different climatic

environments, including Alpine and Tropical regions, on the impact that topographical

features have on rainfall distribution. In the tropical regions, namely Tropical South Pacific

(TSP) and the volcanic island of Tahiti, a strong orographic influence is apparent, resulting in

considerable variation in yearly precipitation that corresponds to substantial changes in the

natural vegetation patterns (Terry & Wotling, 2011). In both regions topography is recognised

as being the major influence on the spatial distribution of precipitation. Wotling et al. (2000)

speculated that topographical relief is directly proportional to rainfall intensity and hence the

steeper the relief, the higher the rainfall intensity. To prove this some deterministic modelling


17

at event-scale was carried out in order to help predict the variations in space of rainfall

intensities (Wotling et al., 2000). Notwithstanding this study, Diodato (2005) noted that the

very little is known about precipitation pattern in mountainous areas of tropical region

because of the complex topography.

Studies conducted in the Alpine regions of Sweden (Johansson & Chen, 2003) and

the Himalayan Mountains (Bookhagen & Burbank, 2006) found that precipitation distribution

is as strongly influenced by topography as in tropical regions. Topography and relief not only

cause high rainfall bands over mountains but also directly influence the rainfall

characteristics (Bookhagen & Burbank, 2006). Nyssen et al. (2005) noted that topographical

aspects such as steep overall slope gradient and valley aspects control the spatial

distribution of annual rain depth. The development of high intensity rainfall events as a

product of topography plays a fundamental role in the type and depth of rainfall and

subsequently the rainfall erosivity within a region.

An increase in rainfall and rainfall erosivity with altitude is, however, not always

evident. The anticipated increase in rainfall with altitude was not apparent when comparing

rainfall totals measured at different stations, covering an altitudinal range of 1060m to 3165

m.a.s.l, in the KwaZulu-Natal Drakensberg, South Africa (Nel & Sumner, 2007) where an

inverse relationship was found between altitude and the associated maximum rainfall

intensity. Though the mean kinetic energy produced during individual events was similar

throughout the area, the high altitude stations in the Drakensberg recorded lower maximum

rainfall intensities and fewer high intensity events than the lower altitudes stations (Nel &

Sumner, 2007). Individual events at all altitudes in the Drakensberg could potentially detach

soil, however, at higher altitudes on the escarpment a lower percentage of rain falls as

erosive events producing lower cumulative kinetic energy and total rainfall erosivity than at

stations on the foothills. Nel & Sumner (2007) explain that the altitudinal differences in

cumulative kinetic energy and cumulative erosivity is due to the lack of erosive rainfall events

during early and late summer at the high altitude stations on the escarpment, while lower

altitudes experience considerably more erosive rainfall events during this period.

Other studies conducted in mountainous areas display similar results to those found

by Nel & Sumner (2007). Hoyos et al. (2005) found that rainfall intensity in the Columbian

Andes appears to be affected by elevation, with consistently lower rainfall intensity (I30)

values at the rainfall stations with the highest altitude. A similar relationship was found in

Honduras, Costa Rica and Sri Lanka and in all cases there is an inverse relationship

between rainfall erosivity and elevation, with the latter increasing to approximately 1750m,

followed by a decrease to lower values at the highest elevation (2120m) (Hoyos et al., 2005).


18

This is attributed to fewer large raindrops being formed by accretion and coalescence at

higher elevations (Hoyos et al., 2005).

Since many tropical volcanic islands have an environment where there is an

altitudinal and temporal difference in rainfall due to the nature of the topography and its

orographic effects, a few extreme rainfall events with high rainfall intensity can generate the

bulk of the cumulative erosivity (Nel et al., 2013). The stage at which the erosive events

experience the high rainfall intensity also affects the amount of sediment eroded, total soil

loss, type and particle size distribution of soil that is eroded. It is therefore necessary to

identify and understand the impact of rainfall event profiles on the severity of the erosion

experienced in tropical island environments such as Mauritius.

1.6. Importance of ‘event profiles’ on infiltration, runoff and soil erosion

As rain falls onto unsaturated soil, a certain amount of the total rainfall infiltrates into

the soil, at the same time the deficit (total rainfall minus total infiltration) will run-off the

surface (Xue & Gavin, 2008). Water percolating into the slope increases the water content of

the soil and reduces the in-site suction, thus decreasing the infiltration rate of the soil (Xue &

Gavin, 2008). Rainfall intensity is a fundamental control of interrill runoff and erosion

(Parsons & Stone, 2006). Although it is generally assumed that runoff is inversely related to

infiltration, and the influence of rainfall intensity can be understood through its effect on

infiltration (Xue & Gavin, 2008), this assumption is too generalised. First, spatial

heterogeneity of infiltration characteristics vary with soil type, such that of the soil surface

may experience increased infiltration with increased rainfall. Second, increased rainfall

intensity may lead to the formation of soil crusts thereby reducing infiltration. The influence of

soil permeability is also important to infiltration and runoff generation (Parsons & Stone,

2006).

Nonetheless the proportion of the total rainfall resulting in infiltration and run-off varies

continuously during a rainfall event (Xue & Gavin, 2008). Rainfall events commonly

demonstrate repeated fluctuations in intensity, such that peak rainfall rates in an event can

exceed the mean event rate by an order of magnitude (Dunkerley, 2008). The occurrence of

peak intensity can be at any instant during a rainfall event, and these events can be

classified according to where the peak falls within the event duration (Dunkerley, 2008).

Commonly accepted terminology includes describing the rainfall event by its leading quartile,

such as first quartile events, fourth quartile event- similar to that developed by Huff (1967) or


19

using uniform intensity, advanced peak, intermediate peak and delayed-peak distributions

according to Zhang et al. (1997).

An ‘event profile’, as a general description, is used to refer to the pattern of temporal

fluctuations in intensity during a rainfall event (Dunkerley, 2008). Distinct rainfall intensity

fluctuations are neglected by the majority of published studies on soil hydraulic properties

such as infiltrabillity and soil erosion processes (Dunkerley, 2012). Event profiles are critical

as the stage in which an event receives its peak intensity impacts on the severity of the

erosion experienced and the amount of sediment eroded, the total soil loss and the particle

size distribution of the eroded sediment (Parsons & Stone, 2006). Subsequently, intra-storm

attributes of the rainfall events become vital in identifying the influence the ‘event profile’ has

on the rainfall erosivity and the potential soil erosion risk which an event causes. Xue &

Gavin (2008) emphasise that the depth of infiltration and runoff is sensitive to the rainfall

event profiles, as event profiles showing an early peak generate more infiltration and less

runoff than events with late peaks in duration. This leads to higher runoff rates, soil loss and

larger particle sizes being eroded (Parsons & Stone, 2006). Therefore, in some cases the

rainfall event profile and not the mean rain rate most noticeably control the relative

magnitudes of infiltration and runoff in a real event (Dunkerley, 2012).

An intra-storm analysis on extreme erosive events was conducted in the KwaZulu-

Natal Drakensberg by Nel (2007) who determined that although the extreme events exhibit

temporal variability in rainfall depth, more than 80% of rainfall generated by extreme events

was received within the first 300minutes of the events. Rainfall intensity received from the

extreme events also exhibits temporal variability and none of the extreme events displayed

constant intensity over time (Nel, 2007). This is significant as Parsons & Stone (2006)

indicated that rainfall events with constant-intensities yield lower soil loss than the events

with varying-intensities. The ‘event profile’ in relation to the rainfall event intensity also

influences the clay-content of the eroded materials (Parsons & Stone, 2006). A study by

(Stocking & Elwell, 1976) indicated that the magnitude of the peak intensities within the

‘event profile’ is most critical to the erosional process and sediment transport peaks in

response to intense rainfall (Nel, 2007). Though it is generally accepted that the erosivity of a

rainfall event is affected by the intra-storm distribution of the rainfall intensity, it has only

recently been incorporated into soil-erosion models (Parsons & Stone, 2006).

Despite previous studies investigating storm kinetic energy and erosivity on Mauritius,

no studies have investigated the intra-storm distribution and the general climatology of

rainfall parameters in tropical island environments. Therefore, the general aim of this study is


20

to investigate the within-storm distribution of rainfall depth, extreme rainfall intensity and

cumulative kinetic energies on the tropical island of Mauritius in the Indian Ocean.

1.7. Aim and Objectives

1.7.1. Aim

The aim of this research project is to conduct an intra-storm analysis of the rainfall

characteristics and general climatology of erosive events on Mauritius. To achieve the above,

the following objectives have been set out:

1.7.2. Objectives

i. To identify the top twenty erosive events (based on the ‘total kinetic energy

generated’) between 2004 to 2008 at six automated weather stations situated in the

western and central regions of the island.

ii. To describe the general rainfall and climatological characteristics of the top twenty

erosive events at each automated weather station;

iii. To provide a comprehensive intra-storm analysis of each event;

iv. To contrast the spatial and temporal differences between the automated weather

stations.

1.8. Project Outline

This research project is divided into six chapters. In this chapter an introduction and

literature review on the concept of rainfall induced soil erosion, rainfall erosivity and the

relationship between rainfall intensity and kinetic energy within the context of a tropical island

environment with an elevated central interior was presented. Chapter 2 provides information

pertaining to the study area and the background to the geographic location and extent of the

island, the geological history, geomorphology, climate as well as the pedology and land use

of the island. Chapter 3 outlines the methodology followed and used in the analyses of the

data used in this project. Chapter 4 presents the results produced following the described

methodology, followed by a detailed discussion and interpretation of the results in Chapter 5.

Finally, Chapter 6 highlights the main conclusions drawn from this project as well as the

recommendations and suggestions for future studies.


21

Chapter 2 : Study Area

Mauritius is located in the Indian Ocean basin and forms part of the Mascarene

Islands, along with Reunion and Rodrigues (Johnson et al., 2010). The island is located

between the latitudes 19˚58.8’S and 20˚31.7’S and the longitudes of 57˚18.0’ E and 57˚46.5’

E (Figure 2-1), near the edge of the southern tropical belt (Dhurmea et al., 2009). Mauritius is

approximately 800km east of Madagascar and nearly 2000km off the coast of continental

Africa (Ng Cheong et al., 2009). The closest island is the French island of Reunion located

200km to the southwest (Johnson et al., 2010).

Figure 2-1: Location map of Mauritius (after Saddul, 1995)

The island’s topography is defined by mountains and hills, plateaus, river valleys and

plains (Nigel & Rughooputh, 2010a). Mauritius is elliptically shaped with a land area of


22

1860km2, a major axis running NNE to SSW direction of approximately 61km and a minor

axis of approximately 46km running in a NNW to SSE direction (Ng Cheong et al., 2009;

Figure 2-2). The island’s coastline is 372 km long (Nigel, 2011) and has a maximum

elevation of 828m.a.s.l at Piton de la Petite Rivière Noir. Port Louis is the capital of Mauritius,

and Curepipe is a major population centre in the interior. Main airport is Sir Seewoosagur

Ramgoolam International Airport (SSR Airport) located in the south eastern region of the

island (Figure 2-2). Mauritius’ most distinctive feature is the central plateau, at an

approximate elevation of 600m (Staub et al., 2014), bordered by the remnants of the primary

shield volcano, rising steadily towards the southwest of the island (Nigel & Rughooputh,

2010b; Figure 2-2). The physical template and characteristics can be summarised as follows.

Figure 2-2: Locality and elevation map with the location of the capital Port Louis, SSR International Airport and Curepipe, a major population centre (Anderson, 2012). Island transect details are shown in Figure 2-3.

SSW

SSE

NNW

NNE


23

2.1. Geology

All of the Mascarene Islands are the summits of great volcanic cones that emerged

out of the ocean floor. Mauritius is composed from remnants of a massive shield volcano

covered by younger volcanic rocks. The geology can be described as being entirely of

volcanic origin and is composed of basaltic rock with differing densities due to the intricate

nature of its formation, except for the beaches, coral formations of the reefs, the alluvial

deposits at the coast and the limited extents of alluvium (e.g. estuary of Black River) (Proag,

1995; Le Roux, 2005; Newsome & Johnson, 2013). The brief volcanic chronology of

Mauritius, described by Proag (1995), is the result of two major activity periods separated by

a gap of 5.5 million years:

― Emergence and Older Series : 10 to 5 million years ago giving rise to the old lavas

― Intermediate and Younger Series : 3.5 million years to 25,000 years

Hence, according to Proag (1995) the formation of the island should be considered in

four volcanic phases:

1. The Breccia Series or Emergence Period of the island 10 to 6.7 Million Years (MY)

Before Present (B.P.)

2. The Old Series – Old Lavas (6.2 to 5million years)

3. The Intermediate Series – Early Lavas (1.5 million years)

4. The Younger Volcanic Series – Late Lavas (700,000 to 25,000 years)

Emergence period of the island, 10 to 6.7 million years ago, started as a rift in the

ocean floor that acted as an eruptive fissure emitting volcanic product such as tuff, boulders

and breccia flows, hence this brecciated series corresponds to the first phase of the old

series (old lavas). The original foundation of the island, characterised by a bulge in the

structure, was made up of pillow lava. Subsequently, a 500,000 year period of relative calm

followed, causing subsidence to occur in the centre of the island as the underlying magma

decreased in pressure (Proag, 1995).

During the second phase or Old Lavas, 6.2 to 5 million years ago, the island

developed a circular, shield shape characterised by a single volcano. The established part of

the big strato-volcano is a vast dome of 40km diameter; 900m high elevation emerged out of

the ocean. Poorly developed minor structures of 5-30m thick compact basalts were radially

emitted from the strato-volcano and covered most of the lava from the Emergence Period

(Proag, 1995). While this period was marked by occasional periods of lava flow (50-100m


24

thick) the summit of the shield stretched out and crumbed, creating a caldera of 24km in

diameter (Proag, 1995).

Intermediate series or Early lava developed following a period of relative geological

calm (approximately 1.5 million years), coupled with intense erosion and volcanic activity

from smaller vents. This period lasted from 3.5 to 2 million years ago (Proag, 1995). Early

Lava is comprised mostly of compact olivine basalts along fissures and vent, and is exposed

only in the south west of the island. Early Lavas responsible for shaping the central uplands

and coastal plains were emitted from approximately 25 vents across the central uplands at

20˚N, corresponding to a series of fault lines on the island. Lava emitted during this series

make up 35% of Mauritius and very compact in their composition (Proag, 1995; Nigel, 2011).

It is believed that the intermediate phase ended about 1.9 million years ago, and was

subsequently followed by a period of inactivity extending over 1.2 million years (Proag,

1995). Renewed eruptions occurred between 700,000 to 25,000 years and relate to the last

period of volcanic activity on the island. These lava flows erupted from a chain of

approximately twenty vents, where Curepipe Point is the largest and highest (685 m)

(Saddul, 1995). This activity corresponds with the younger series, or the Late Lavas (1.9

million to 25,000 years ago). Late Lavas occupy the greatest part of the island and are

distinguishably from the Early Lava through their highly vesiculated appearance (Saddul,

1995). This period experienced sea levels fluctuations and intense cutting of valleys and

gorges that altered the topography of the island (Proag, 1995). Non-volcanic materials such

as coral reefs, sandy beaches, sand dunes and some consolidated coral and shell debris in

isolated remnant raised beaches are also present. The outcome of the collapsed caldera and

recent lava follows gave rise to the topography of the island, comparable to that of most

tropical islands (Saddul, 1995).

2.2. Topography and hydrology

Mauritius’ topography has been described as a central upland surrounded by

mountain ranges, isolated peaks and plains forming a bowl with chipped rims, filled with

younger lavas (Proag, 1995). According to Parish & Feillafe (1965) Mauritius has three clear

topographic patterns corresponding to the age of the parent lava (also see Proag, 1995).

First, the Old Lava Series gave rise to the mountain ranges that contain the highest peaks

culminating on the south west side of the island within the Rivière Noire- Savanne mountain

range that has Piton de la Petite Rivière Noir as its peak at 828 m.a.s.l. Second, the


25

Intermediate Lavas correspond with the gentle rolling topography and deeply incised rivers

with terraces and stabilised gullies. Third, the topography consistent with the Late Lavas are

characterised by several rocky areas that are almost completely absent of surface drainage

and are dominated by the vents that gave rise to them.

The elevated central region, also referred to as the central uplands or central plateau,

lies higher than approximately 600m and directly influences the island’s climate. This

elevated region is as a result of historical geological processes as well as more recent

erosive processes involved in the formation of the island. Figure 2-3 shows the island profiles

from SSW to NNE, and NNW to SSE. The central plateau of elevated region is evident on

the SSW to NNE transect as well as Curepipe point (approximately 685m) which is the

largest and highest volcanic point on the island. Location of the island profile transects is

indicated on Figure 2-2.

Figure 2-3: Island profiles from SSW to NNE, and NNW to SSE (after Saddul, 1995; taken from Le Roux, 2005). Location of the island profile transects is indicated on Figure 2-2.

Topography of Mauritius is characterised by five geomorphological domains (Proag,

1995; Saddul, 1995; Figure 2-4):

1. The ‘mountain environment’, was formed by massive lava flows of the Old Lava Series

that resulted in a discontinuous ring encircling the central uplands, with isolated peaks

and plains forming a bowl with chipped rims (Proag, 1995). As a consequence of this

formation, three distinct mountain ranges exist:

The Port Louis-Moka-Long Mountain Range in the northwest


26

The Black River-Savanne Mountain Complex and,

The Bamboo Mountain Massif.

2. The central plateau, also referred to as the ‘central uplands’, is comprised of land

contained by the caldera of the island, and includes flat terrain to subdued plateau like

topography to a variety of undulating uplands, with a mean elevation of between 300 to

400m, rising to approximately 600m along the dissected plateau in the southern part of

the island and gently sloping relief that merges into the coastal lowlands of the east.

3. The ‘southern uplands’, where the Early Lavas are situated, can be characterised by

terrain consisting mostly of multi-formed segments of convexo-concave slopes and

comprises all the land above the 500m contour.

4. The ‘recent lava plains’ areas all lie below the 200m contour and include the coastal

plains and inland slopes, with undulating slopes between 2 – 13% as well as vast

expanses of rocky surfaces.

5. The ‘coastal environment’ contains sandy beaches, the lagoon, rocky coastline and coral

reefs just below or above sea level. Coral reefs surround the island except along the

western and southern coast and at the mouth of some rivers (Saddul, 1995; Proag 1995).

Figure 2-4: Main geomorphological domains on Mauritius (after Saddul, 1995; taken from Anderson, 2012)


27

The structure of the hydrological resources on Mauritius is largely dependent upon its

predominantly basaltic geology. However, the complex nature of the island’s formation gave

rise to basalt of various densities ranging from the impermeable compact basalt to highly

porous basalt. The latter acts as a water collector therefore aquifers of Mauritius have a high

permeability in excess of 10-5m/s (Proag, 2006). Hence, the texture and type of formation

from the different volcanic activities determines the natural infiltration rates, the contribution

of rainfall recharge to aquifers and also the amount of runoff. There are five main aquifers on

the island and groundwater plays a major role in sustaining flows in the rivers (Proag, 2006).

Mauritius is drained by several river systems spread on the gently rolling topography

of the Intermediate Lavas of the Recent Volcanic Series (Nigel, 2011). The majority of the

rivers spring from the central plateau and flow radially to the sea, yet their distribution is

highly irregular (Proag, 2006). Mauritius has been divided into catchment areas or

riverbasins comprising: 25 main basins, each corresponding to a main river, and 22 minor

ones and coastal zones drained by several streams (Proag, 1995). Catchment areas vary in

size from 3 – 64km2 (Proag, 1995). The island has two natural lakes: Grand Bassin and

Bassin Blanc together with eleven reservoirs, Mare aux Vacous, as seen in Figure 2-5, being

the largest. Proag (1995) noted that the northern, eastern and south-eastern parts are

deprived of surface drainage and lack the presence of major rivers due to the low gradient of

these areas.

Figure 2-5: Catchment boundaries, major rivers and water bodies on Mauritius (WRU, 2007; taken from Anderson, 2012)


28

2.3. Pedology (soils)

Remarkably similar geology, climate, soils and cropping characteristics between

Mauritius and some parts of the Hawaiian Islands led to the acceptance of a classification

system used for soil survey of the territory of Hawaii for the island of Mauritius (Parish &

Feillafe, 1965; Arlidge & Wong You Cheong, 1975; Williame, 1984; Proag, 1995; Le Roux

2005; Nigel, 2011; Anderson, 2012). The classification of soil types are based on Great Soil

Groups with subdivisions and Families based on differences in rainfall or parent materials

portrays the soil types of Mauritius in relation to their age or development stage (Pears, 1985

cited in Proag, 1995: 21; Le Roux, 2005; Figure 2-6).

Boundaries between soil groups are diffuse, and are differentiated by chemical rather

than morphological criteria. Mauritian soils developed virtually exclusively on olivine basaltic

lavas or highly vesiculated basaltic lava (Proag, 1995). As a result of the basaltic origin of the

Figure 2-6: Classification of Soils in Mauritius (Proag, 1995)


29

soils, no series subdivisions occur (Williame, 1984; Le Roux, 2005). Subsequently, the soils

of Mauritius can be placed in two categories: Mature ferralatic soils (latosols), and immature

latosolic soils (Figure 2-6). First, Mature Latosols originate from the decomposition of basaltic

lava rock, and can be further sub-divided into families- Low Humic, Humic, Hydrol Humic and

Humic Ferruginous that characterize areas of similar climate and topography, thus similar

soils. No undecomposed minerals are present in this soil complex. Second, the Immature

soils (Latosolic), are characterized by the presence of angular clasts and gravel of vesicular

lava, thus these soils contain minerals that are still in the process of weathering (Proag,

1995).

Mauritius’ natural soil fertility composition is low because of a noticeable deficiency in

nitrogen, phosphate and potash (Proag, 1995). Soil fertility decreases with increasing rainfall

and age of the parent material (Figure 2-6). Additionally, rainfall increases with elevation, and

therefore, a vertical zonality of fertility occurs which is correlated with the vertical zonality of

Mauritian soil. For example, the Latosolic Reddish Prairie soils are less fertile at high rainfall

levels with excessive drainage and shallow depth, than Humic Latosols occurring at low

rainfall levels and limited drainage (Proag, 1995; Le Roux, 2005).

The soils found on Mauritius offer a good example of zonality. Zonality is the

progressive intensity of weathering and soil development with increase in the intensity of soil

forming factors, notably rainfall. Soils susceptibly to heavy rainfall are variable.

Subsequently, the islands soils are classified further into three soil orders: Zonal, Intrazonal

and Azonal soils. Proag (1995) describes the Mauritian soils as follows (also see Le Roux,

2005; Nigel, 2011): Zonal soils developed predominately from the Intermediate Lava under

mean annual rainfall of 1000 – 4000mm of rain. Intrazonal soils developed on the Late Lavas

under conditions where the effects of climate and vegetation are obscured by local factors of

the environment such as relief, drainage and composition of the parent material. Azonal soils

have little or no profile development, apart from some accumulation of organic matter in the

surface horizon. Further discussion of these soil groups can be found in Parish & Feillafe

(1965), Arlidge & Wong You Cheong (1975), Williame (1984), and Proag (1995).

The three soil orders are distributed across the island, the Zonal Soils constitute

approximately 33% of the island’s surface area, Intrazonal soils makes up 36% and Azonal

soils makes up 18%, with the remainder of the island covered in various water bodies (Nigel,

2011) as shown in Figure 2-7.


30

Figure 2-7: Soil map of Mauritius from DOS and MSIRI (1962), taken from Nigel (2011)

2.4. Climate and weather

Mauritius has a tropical maritime climate that is influenced by the surrounding Indian

Ocean (Saddul, 1995). The island is under the influence of atmospheric circulation,

developing from the two primary zones: a low pressure zone in the north and a high pressure

zone in the south (Williame, 1984; Nigel, 2011). Substantial variations in rainfall and other

climatic characteristics are due prominently to differences in elevation, distance from the

coast, windward-leeward locations, cold fronts, the position of the Inter Tropical Convergence

Zone (ITCZ), occasional convective storms and seasonality (Fowdur et al., 2014). The

average annual temperature is 22˚C: July being the coolest month with temperatures ranging

from 16˚C in the central uplands to 22˚C in the coastal areas (Proag, 1995). February is the

warmest month with temperatures varying from 20˚C (central uplands) to 28˚C (coastal).


31

Average sunshine per day varies between 7 and 8 hours depending on the season (Padya,

1989).

For the majority of the year, Mauritius lies within the South East Trade Winds but

throughout the warm and rainy summer months (November to May) the most important

climatological features are tropical cyclones and depressions associated with the seasonal

movement of the ITCZ (Dennett, 1978). However, during the comparatively dry winter period,

(April to October) the South East Trade Winds dominate and are accompanied with few

rainfall events, which are associated with frontal systems (Nigel, 2011).

Another importance influence on weather patterns of Mauritius is the spatial and

temporal distributions of mean Sea Surface Temperature (SST). Typically, the mean SST is

higher on the western part of the tropical and subtropical Indian Ocean, and is largely related

to the warm westward-flowing south equatorial current. The SST near Mauritius varies from

22˚C in September to 27˚C in March. Even though intra-annual variations in SST are small,

they indicate seasonal changes such as summer being the hot and wet season, and the

winter is warm and dry. Inter-annual variations in the mean SST are also reasonably small

(Padya, 1989; Nigel, 2011).

2.4.1. Weather systems in Mauritius

Eight weather systems prevailing over Mauritius during a typical year have been

identified by Fowdur et al. (2014). These are windfields over Mauritius, cyclonic activity,

ITCZ, cold fronts, anticyclones, sea breezes, easterly wave perturbations in the lower

troposphere, and cloud masses derived from upper level winds. Particular focus has been

given to cyclones, due to their destructive nature and high kinetic energy and subsequent

erosive nature. The weather systems are described below:

2.4.1.1. Windfields over Mauritius

Mauritius is located at the edge of the tropics, within the belt of trade winds. Winds on

the island are influenced by two main regimes, The South East Trade Winds (easterlies) and

the North West Monsoon Winds (westerlies). Westerlies occur predominately in the summer

months when the ITCZ lies over the south of Mauritius. The months April to May are

dominated largely by easterlies (Fowdur et al., 2014). Padya (1989) identified that by the end

of June, the easterlies show an expansion southwards. South east trade winds (easterlies)

supply the dominant moisture advection and combined with topographic uplift and sea-

breezes are responsible for the steep rainfall gradient and the west coast rain-shadow effect


32

which impacts upon erosion risk and rainfall erosivity distribution (Fowdur et al., 2014; Staub

et al., 2014).

2.4.1.2. Cyclonic activity

Mauritius typically experiences storms ranging in intensity from tropical depressions

to tropical cyclones in any single cyclone season, yet intense or very intense tropical

cyclones are relatively rare (Parker, 1999). Cyclonic events are considered erosive in nature

and are a climatic factor of substantial importance to agricultural production in Mauritius

(Proag, 1995). There are, on average, 11 atmospheric depressions each summer that

develop in the south western part of the tropical cyclone belt in the Indian Ocean (Padya,

1989). Due to the island’s position in the Indian Ocean, cyclones most commonly form

northwest of Mauritius and follow relatively erratic paths in a south-westerly direction (Figure

2-8) (Padya, 1989). The incidence of intense or very intense tropical cyclones aligning on the

main island of Mauritius is estimated at between 1:8 and 1:15 years (Parker, 1999).

The cyclonic period traditionally extends from November to March. Average rainfall

during these tropical cyclones is 245mm, but variations are largely dependent on the nature

and intensity of the cyclone and on the distance of the cyclone while passing the island (Le

Roux, 2005; Nigel, 2011). Typically, rainfall amount is highest (>300mm/day) when the

tropical cyclone passes close enough for the island to fall under the influence of the inner

convection band and when the passing is not so close, the rainfall amount is moderate but

substantial (Padya, 1989; Nigel, 2011). From 20-22 January 2002, Mauritius was hit by the

most recent very intense cyclone (Dina) and saw 488mm of rain being recorded at Vacoas

during the event with wind gusts of 209km/h. As is evident in Figure 2-8 each of the major

cyclone events has a specific trajectory.

During the study period, 2004 – 2008, numerous tropical cyclones passed within the

vicinity of Mauritius (Météo-France, 2015). Four of the most noteworthy cyclonic events

included Darius (classified as a ‘Severe Tropical Storm’ or Class I cyclone) from 31

December 2003- 03 January 2004. The second most noteworthy event during this period

was Hennie (classified as a ‘Severe Tropical Storm’ or Class I cyclone) in March 2005 (22-

24th). Hennie passed 60km South East of the island and had maximum wind speeds of

112km/h. Third was Diwa (also classified as a ‘Severe Tropical storm’ or Class I cyclone) in

March 2006 (3-4th) and passed 220km North North West of the island with winds as high as

126km/h. The fourth was Tropical Cyclone Gamede during February 2007 (22-25th), which

passed 230km North West of the island with windspeeds as high as 158km/h (MMS, 2014d).


33

Figure 2-8: Trajectories of most severe tropical cyclones affecting Mauritius from 1892-2005, with 2004-2008 cyclones presented in the text (MMS, 2014d).

2.4.1.3. Inter Tropical Convergence Zone (ITCZ)

The ITCZ is considered a meteorological boundary between the northern and

southern hemispheres. It is also regarded as a boundary where the North West monsoon

winds (westerlies) and the South East Trade winds (easterlies) converge. Very little transfer

of mass and energy occurs across the ITZC in the atmosphere and the commonly accepted

opinion is that the ITCZ arranges itself in such a way that the thermal processes taking place

in the atmosphere achieve equilibrium on each side of it. Subsequently, the ITCZ affects the

weather in Mauritius in the summer months of January to February and occasionally

December and March, during its southerly seasonal migration (Fowdur et al., 2014).

The ITCZ is closest to Mauritius between January and March when warm season

depressions affect the island by causing high rainfall (Staub et al., 2014). Under the influence

of the ITCZ, even the west coast which is sheltered from the south-easterly trade winds

experiences convection showers. Although the month of April in Mauritius is known as the


34

transitional month at the end summer, wet-season conditions and higher rainfall persist on

the elevated central plateau and the eastern mountains (Padya, 1989; Staub et al., 2014).

The winter months, May to October, are drier than the summer months as the ITCZ is

located north of the equator.

2.4.1.4. Cold fronts

Two air-masses have been recognised in the southern Indian Ocean: the Maritime

Tropical air mass to the north over the warmer seas and the Maritime Polar air mass over the

cooler waters to the south of the Oceanic convergence. Each air mass has distinct

characteristics from the source region and the boundary between the two distinct air masses

is constantly moving. An active advancing cold front is when a general northward movement

of the boundary between the maritime polar and maritime tropical air masses occurs. Such

occurrences of colder air originating in the far south often reach Mauritius, except during the

warmer parts of summer, namely January and February (Fowdur et al., 2014). Throughout

the winter months (May to October), the passage of cold fronts is generally associated with

an increase in convective activity and subsequent rainfall over Mauritius, particularly in areas

with orographic characteristics (Padya, 1989).

2.4.1.5. Anti-cyclones

At the longitude on which Mauritius is found, the sub-tropical belts of anticyclones are

situated near 30˚S in the winter, retracting pole-ward to about 35˚S in the summer. The

strongest anticyclones occurring in winter have a central pressure exceeding 1040 HPa

(Fowdur et al., 2014). During the winter period the majority of the rainfall is caused by the

resultant advection from orographic uplift due to the presence of anticyclones (Padya, 1989).

2.4.1.6. Sea breezes

Sea-breezes occur when the land heats a layer of air close to the ground surface

causing the warm air to rise. As this process continues, a deeper and warmer layer develops

over the land surface. Eventually, the temperature of the surface layer has risen sufficiently

to produce a significantly lower pressure over the land than over the sea which experiences

a relatively higher pressure. The subsequent pressure difference initiates a breeze from the

sea to the land. Fowdur et al. (2014) explain that as a rule, in Mauritius, a ‘prevailing’ east-

south-east wind is subject to its own synoptic space and time variation. Therefore, over the

east and south coastal area, the sea breeze will help to accelerate the low-level trades and

will affect their direction. On the eastern side of the country, the winds produced by sea-

breeze can be felt strengthening during the mid-morning and continues blowing as long as

the land is kept warmer than the sea by insolation. Occasionally, the whole stretch of sloping

land in the south and south-east, continuing three to four kilometres inland, is shaded by

clouds the entire day and precipitation can be experienced (Fowdur et al., 2014).


35

2.4.1.7. Easterly waves perturbations in the lower troposphere

Perturbations of the lower troposphere, give rise to heavy rainfall over parts of

Mauritius during the summer months, regardless of the depth of the surface easterlies.

Perturbations of this nature, even when characterised as shallow, have a distinct wave-like

character and are commonly encountered in the south-west Indian Ocean, between 70˚E

and 50˚E. Winds related with these perturbations range in the order of 5-8ms-1 The

thunderstorms develop from the perturbations generally cause considerable amount of

rainfall (Fowdur et al., 2014).

2.4.1.8. Cloud masses and upper level lows

Frequently, Mauritius and its neighbouring islands also experience cloudy and rainy

conditions without an identifiable synoptic system being detected. In such cases, satellite

imagery depict the cloud system as either small clouds patches (2 degrees or so in diameter)

in a line with an almost meridional (north-south) orientation or large masses (ten degrees in

width). These air masses have varying degrees of activity. On occasion heavy rainfall and

active thunderstorms take place during these synoptically unexplained weather systems

(Fowdur et al., 2014).

2.4.2. Rainfall

2.4.2.1. Mean annual rainfall depth and spatial variations

Rainfall on the island is seasonal and spatially variable with a wet season from

November to April and a dry season from May to October. Annual mean rainfall for the island

is approximately 2112mm, with 70-79% being recorded in the rainy summer season (Le

Roux, 2005; Nigel & Rughooputh, 2010b). The long-term mean annual rainfall indicates that

the eastern side of the island receives approximately 1200mm rainfall, with the elevated

central receiving up to 4000mm, and only 600mm on the western coast (WRU, 2007). Figure

2-9, taken from Nigel (2011) below shows a rainfall map for Mauritius produced by Seul

(1999) using data consisting of the mean annual rainfall over a period of 30 years (1961-

1990) for 194 gauging stations. It should be noted that the number of rainfall stations on

Mauritius has since increased to in excess of 256 stations (MMS, 2014a).

Mauritius as a land mass enhances rainfall averages across the island. The raised

interior of the island is responsible for the noticeable spatial difference in rainfall from east to

west on the island.


36

Figure 2-9: Mean rainfall (1961-1990) and mean annual rainfall amount (Nigel, 2011)

Spatial variations of the rainfall on Mauritius are attributed to orography created by

the eastern mountain ranges and the ‘ridges’ of the central plateau. The plateau acts as a

barrier to the South East Trade Wind which divides the plateau into two regions, the

windward and leeward regions. Higher totals of rainfall are received on the windward side

and a ‘rainshadow’ is evident on the leeward side (Nigel, 2011). The western part shows the

effect of the depletion of water vapour on the windward slopes, with the descent of the air of

the leeward slopes (Fowdur et al., 2014). Hence, Padya (1989) noted that the eastern and

southern regions have fairly similar rainfall patterns throughout the year as they receive the

oceanic air with its high moisture content intact, benefiting from the forced uplift resulting

from its passage over the sloping lands and hills. Rainfall days a year are always greater

than 200 for zones where the annual rainfall amounts exceeds 2000mm and decreases

progressively in drier zones to reach about 60 rainfall days a year on the western coast

(Nigel, 2011). Consequently, it is the combination of the orographic effect in conjunction with


37

the frictional effect of air movements that is responsible for the local rainfall on the island

(Padya, 1989).

The effect of relief on the spatial distribution of rainfall across Mauritius is shown in

Figure 2-10, where an exaggerated vertical scale section is made on a cross-section from

Beau Vallon on the south-east coast to Medine in the west. This reveals the striking effect

that relief has on the spatial rainfall distribution; with the transect lying roughly in the direction

of the prevailing south-easterly trade winds. The curve shows that the annual rainfall is about

1200mm on the south-east coast and increasing to over 4000mm ahead of the highest

ground of the Central Plateau, then slowly decreases to around 600mm at Medine in the

west making the asymmetry of the rainfall distribution across the island strongly apparent

(Rughooputh, 1997). Therefore, rainfall significantly increases from the coast to the interior.

This rainfall gradient is due to the orographic effects caused by the south-eastern mountain

range and the central uplands (Fowdur et al., 2006).

Figure 2-10: The effect of relief on rainfall with annual rainfall along a 40km ESE-WNW line at selected sites (Padya, 1989)


38

2.4.2.2. Inter- and intra-annual variations in rainfall

Rainfall on the island is of great importance as it is the only source of water for rivers,

agriculture and human needs as water from the ocean is not exploited for the population. The

inter-annual variations in rainfall amount are substantial due to the passage of cyclones,

which could increase the “normal” monthly rainfall amount received by 2-3 times (Willaime,

1984; Padya 1989, Nigel 2011).

Annually, the wet season occurs between November until the end of April, with

February being both the hottest and wettest month, and the dry season from May to October,

with October being the driest month (Proag, 1995; Nigel & Rughooputh, 2010b).

Approximately 60-70% of the mean annual rainfall is received during summer, and 30% of

the mean annual rainfall is received during winter thus rainfall is seasonal (Nigel &

Rughooputh, 2010b). Nearly 40% of the annual total rainfall falls between January and

March, coinciding with the southward migration of the inter-tropical convergence zones

towards the subtropical latitudes and the occurrence of tropical cyclones (Staub et al., 2014).

Rainfall on the island most commonly comes from the orographic lifting of the moisture found

around the circulation of weather systems that move over the island as well as the

occasional cyclones and depressions (Dhurmea et al., 2009).

In addition to these seasonal movements, the summer period is also influenced by

tropical cyclones, most of which do not make landfall (Padya, 1989). The winter season is

subjected to the south easterly trade winds and frontal systems (Padya, 1989). Potential soil

erosion is highest during the early part of the wet season when there is high rainfall present

and the vegetation cover is not yet adequate to protect the soil from erosion (Morgan, 2005).

Drought conditions can be experienced if the island lies outside the path of these tropical

depressions (Proag, 1995).

2.4.3. Climate classification

Mauritius’ climate is described by Proag (1995) and Saddul (1995) as humid,

subtropical and maritime as a result of its location at 20˚S latitude, small size, lack of extreme

elevations and distance to continents. Owing to variations in diurnal exposure, elevation and

distance from the sea, a succession of island scale climatic regions or microclimates exist

(Nigel, 2011). Notwithstanding the small size of the island, Padya (1989) identified at least 27

microclimates caused by substantial variations in the climatic characteristics of Mauritius.

Hence, the raised topography of the island results in orographic lifting occurring, contributing

to the creation of several micro-climates (Nigel & Rughooputh, 2010a). Utilising the variations


39

in temperatures and rainfall from one region to another allows for climatic subdivisions of the

island into three general zones: subhumid, humid and superhumid. The subhumid zone is

restricted to low altitudes on the western coast and the northern plains and receives less

than 1250mm per annum. The humid region receives precipitation between 1250mm and

2000mm per annum and characterises the intermediate altitudes in the west and north as

well as the low altitudes in the east and south, and the superhumid region which prevails

above approximately 400m (450m in the west) where rainfall exceeds 2000mm per annum.

Humid regions have a rainfall evaporation balance, while evapotranspiration exceeds

precipitation in the subhumid regions (Le Roux et al., 2005).

The microclimate zones are attributable to orographic lifting: the central uplands are

super humid (46% of the total area), east and south are humid regions (19%), and a small

area in the west is defined as semi-arid (Fowdur et al., 2006; Nigel & Rughooputh, 2010a).

On average, the humidity across the island is 80% which remains constant throughout the

year. Humidity is higher in the southern central regions, which consists of the central plateau,

the highlands and lowest near the coast. The microclimatic zones and humidity influences

the indigenous vegetation within regions (Anderson, 2012).

2.5. Land use and vegetation

Mauritius was described by 17th century travellers as an island with tropical forests

and jungle from mountain tops to the sea (Parish & Feillafe, 1965). The present land use and

vegetation has been heavily influenced by people (Nigel, 2011). Prior to colonization in 1638,

the island was completely covered by wet and dry evergreen forests, shrub and plain

savanna. However, subsequent to human colonization approximately 95% of the native

vegetation area has been removed, including the complete destruction of the native palm

savannah. There are 6 forest-living native passerines remaining on the island that are

protected by legislation. The main vegetation remaining on the mountain slopes in the south-

western and central-eastern is the moist montante tropical evergreen forest, as well as dry

forests which dominate the rain shadow regions (Safford, 1997). Due to inadequate

regeneration of the native vegetation in conjunction with the high invasion rate of the exotic

vegetation and urbanisation has left the native vegetation is a stressed state (Lorence &

Sussman, 1986) evident in the tiny isolated fragments of vegetation remaining today. A study

done by Saddul (2002) indicated that 55% of the island’s land was classified as cultivated,

27% was forest, 11% was scrub, 6% was urbanized and the remaining 1% accounting for

miscellaneous land uses.


40

Vast amounts of Mauritius’s indigenous vegetation, such as ebony forests, bois d’

olive and aloes have been removed to accommodate the extensive cultivations which occur

on the island, including sugarcane, tea, vegetables and fruit plantations (Nigel &

Rughooputh, 2010b). In 2012, 85% of the arable land (approximately 69000ha) was used for

sugarcane cultivation (Soobadar & Kwong, 2012), despite the Mauritian government

promoting a policy of agricultural diversification (Le Roux, 2005). The Mauritian economy is

heavily dependent upon the export of sugarcane and its bye-products, hence the high

percentage of cultivation. Soil loss under sugarcane is, however, relatively low compared to

other vegetables as the soil under the sugarcane is not disturbed during harvest and a dense

cover is provided within less than two months after regrowth or planting (Le Roux et al.,

2005). Good land management is vital as erosion hazard maps show that potential soil

erosion increases greatly when the canopy cover of sugarcane decreases (Kremer, 2000).

The two regions under investigation in this project have noticeably different types and

densities of vegetation cover due to the different rainfall regimes they are subjected to. The

western region (receiving approximately 600mm annually) where the automatic weather

stations of Albion and Beaux Songes are situated is comprised of shrub-like, grassy and

sparse vegetation. The central interior (receiving approximately 4000mm annually) where

Arnaud, Monbois, Grand Bassin and Trou aux Cerfs are situated has discernibly denser and

lusher vegetation. The difference in vegetation cover and density is evident from Figure 2-11

and Figure 2-12. These photographs were taken on the same day in June 2010.

©R. Anderson (2010)

Figure 2-11: Landscape near the station at Albion (Photo taken 28 June 2010)


41

Figure 2-12: Landscape near Trou aux Cerfs (Photo taken 28 June 2010)

This chapter has provided background information for this study, including information

on the geological history, geomorphology and topography of the island. The vegetation and

land use occurring on the island was also presented. A description of the climate and

weather affecting the island has been included, with particular emphasis on the rainfall and

the eight weather systems prevailing over the island as identified by Fowdur et al. (2014).

The unique pedology and hydrology of the island was also briefly discussed to provide the

context in which the next chapter will continue. To summarise, Mauritius is a diverse island in

terms of a number of factors including topography, climate and weather as well as location,

resulting in the development of several microclimates. These microclimatic areas are

reflected in the diversity of the natural vegetation across the island. The next chapter will

provide the methods and station data used in this project.

© R. Anderson (2010)


42

Chapter 3 : Station Data and Methodology

The purpose of this chapter is to outline the methods and materials used for this

project. It describes the research design followed in the analysis of the data, as well as

highlighting some of the limitations incurred in the methodology. In addition, this chapter

presents the equipment used in obtaining the data used in this project, and lastly the

analyses that were undertaken. As presented in chapter one, the objectives that were set out

are to:

Identify the top twenty erosive events (based on the ‘total kinetic energy

generated’) between 2004 to 2008 at six automated weather stations situated

in the western and central regions of the island.

Describe the general rainfall and climatological characteristics of the top

twenty erosive events at each automated weather station;

Provide a comprehensive intra-storm analysis of each event;

Contrast the spatial and temporal differences between the automated weather

stations.

3.1. Station data

Mauritius has in excess of 256 weather stations spread across the island, covering a

total area of 1860km2 (Proag, 1995). Monitoring the climate variables on Mauritius is done

using three different types of weather stations; namely agro-meteorological stations,

climatological stations and synoptic stations. Agro-meteorological stations monitor and

record standard climatological data which relates to agriculture, livestock breeding and

forestry. Synoptic stations make meteorological weather observations including all

meteorological parameters at fixed time intervals and their recorded observations are

exchanged at the Global Telecommunication System of the World Meteorological

Organisations. The ordinary climatological stations (approximately 30 automatic weather

stations) record rainfall and well as other climatic parameters such as evaporation, humidity,

sunshine duration, temperature and wind are measured by these stations (MMS, 2014a).

To investigate the intra-storm attributes of the erosive events at the respective

stations on Mauritius, high resolution automatic rainfall data from six stations for the period

01/01/2004 to 31/12/2008 (5 years) were provided by the Mauritius Meteorological Services

(MMS) (Figure 3-1). The area of interest for this project includes the central interior and the


43

west coast. Two sites on the west coast of the island fall within the rainshadow, one on the

coast at Albion (12m.a.s.l) and one approximately 4km from the coastline at Beaux Songes

(225m.a.s.l) (Table 3 – 1) were selected. Data were also obtained from rainfall gauges on the

Central Plateau area at Arnaud (576m.a.s.l), Monbois (590m.a.s.l), Grand Bassin

(605m.a.s.l) and Trou aux Cerfs (614m.a.s.l) (Table 3 – 1).

Figure 3-1: Location of selected automated weather stations.

The automatic weather stations used for this study are located at different altitudes to

account for differences in rainfall and associated rainfall erosivity due to orographic lifting

(Table 3-1). Therefore, the analysis of the intra-storm attributes conducted at the two

climatological extremes of Mauritius provides the best coverage of the area of interest from

the available data. As the eastern portion of the island (or elsewhere) did not contain any

automated weather stations at the time of the study, no data were available to complete the


44

altitudinal cross-section. However, the data that were made available sufficiently covered the

driest and wettest rainfall regions of the island.

Table 3-1: Location, altitude and climatic information of the automatic weather stations (also see Anderson, 2012; Mongwa, 2012; Nel et al., 2012)

Two previous rainfall studies were undertaken on Mauritius using the data sets.

Anderson (2012) found a difference in rainfall erosivity experienced on the interior and rain-

shadowed west coast and reported that stations on the west coast recorded 25% of the

erosivity experienced by the stations in the elevated central interior. Large differences in the

number of erosive events, rainfall depth, erosive rainfall totals, seasonality and annual

erosivity total were noted (Anderson, 2012). The study by Anderson (2012) concluded that

the changes in erosivity occurred with changes in rainfall intensities caused by the

orographic effect of the interior. The other study was undertaken by Mongwa (2012) who

found that erosive events on the west coast and central plateau are remarkably different with

regards to frequency, total rainfall generated, duration, total kinetic energy and total erosivity

of individual events. Yet the mean kinetic energy, mean and maximum rainfall erosivity (El30)

and maximum intensities (I30) from the individual erosive events do not show this distinct

differentiation. Both studies (Anderson, 2012; Mongwa, 2012) speculated on the influence

that the elevated central interior has on the spatial and temporal distribution of rainfall across

the island. Thus, by incorporating another rainfall station and analysing the intra-storm

attributes of the erosive events, this study attempts to highlight and also explain the

anomalies identified in the previous studies.

Station Location Latitude Longitude Altitude

(above sea level)

Rainfall Region

Albion West Coast 20˚12.75’ S 57˚24.0’ E 12m Semi-arid

Beaux Songes

West Coast 20˚16.68’ S 57˚24.48’ E 225m Semi-arid

Arnaud Central Plateau 20˚22.8’ S 57˚29.52’ E 576m Super-humid

Monbois Central Plateau 20˚19.8’ S 57˚31.68’ E 590m Humid

Grand Bassin Central Plateau 20˚24.0’ S 57˚29.52’ E 605m Super-humid

Trou aux Cerfs

Central Plateau 20˚17.82’ S 57˚31.02’ E 614m Super-humid


45

3.2. Research instruments

Rainfall data with a high temporal resolution are necessary for accurate calculations

of rainfall erosivity (Yin et al., 2007). Rainfall records used in this project were collected at the

stations by the Mauritius Meteorological Services at six minute intervals using the Précis-

Mécanique R01-3030 Pluviometer with tipping bucket (Figure 3-2). Automated weather

stations are positioned away from any other surface to avoid the readings being influenced

by additional rain splash from other nearby structures as well as limiting any vibrations from

nearby activities or structure from influencing the readings. Total rainfall amounts every six

minutes are downloaded by the system into a Microsoft Excel file. The weather station’s

tipping bucket contains deflectors which are able to reduce losses of rainfall received during

high intensities and also record continuously in both wet and dry conditions. The bowl has a

diameter of 230mm, collecting area of 1000cm2 and a thin rim which minimizes rainfall splash

as well as other weather related errors such as wind, wetting and evaporation induced losses

(Lanza et al., 2005; Alexandropoulos & Lacombe, 2006).

Figure 3-2: Example of the automatic rainfall bucket used by the Mauritius Meteorological Service (MMS) to record rainfall data. Also see Anderson (2012) and Mongwa (2012).


46

Each separate weather station recorded information at the site including rainfall

totals, rainfall intensity (rainfall received every 6minutes), temperature, wind speed and

pressure as a separate Microsoft Excel file on a daily basis. Individual files were

subsequently “stitched” together so that all the months for a single station are consolidated

into a single Microsoft Excel spreadsheet, and all the years for each station is placed into a

single Microsoft Excel file for convenience (Nel, pers comm). Rainfall and non-rainfall days

were separated and non-rainfall days were then removed from the data set. For this project,

a rainfall day was fixed as a day which received ≥1mm of rainfall during a 24 hour period.

For calculation purposes, erosive rainfall events were separated from non-erosive

events the latter which are subsequently omitted. This is routinely done as it enormously

eases the calculation of rainfall erosivity, particularly in terms of reading and digitising rainfall

charts (Xie et al., 2002). Wischmeier & Smith (1978) suggested that rains of less than

12.5mm should be omitted in erosion index computations (these parameters will be

discussed later in further detail). Although natural intensity variations are present from one

event to another, consensus prevails between various studies (Stocking & Elwell, 1976;

Renard & Freimund, 1994; Xie et al., 2002) that a maximum intensity of 25mm/h can be used

as a practical threshold for separating erosive and non-erosive rains. If non-erosive events

are counted, the rainfall erosivity could be either over- or under- estimated (Xie et al., 2002).

Hence, the objective of determining a practical threshold is to omit non-erosive rains to

reduce calculation requirements while obtaining the most accurate possible value for

erosivity (Xie et al., 2002).

3.3. Missing, unreliable and incomplete data

The following records were incomplete at all the stations records; June and July

records from 2004, November and December from 2006, and January, February and March

2007. However, as these records were unavailable from all the stations, direct inter-station

comparisons could still be made as those data do not affect the scope of this project (also

see Anderson, 2012; Mongwa, 2012).

As rainfall and its intensity is natural highly variable, no statistical extrapolation

methods were used to fill gaps created by missing data. Extrapolating the rainfall data could

lead to inaccurate results by over-estimating the number of erosive rainfall events and thus

decreasing the accuracy of the results presented in the next chapter. Additionally, several

rainfall events were excluded from the data as the rainfall recorded during the event was

extraordinarily high and can be attributed to errors in the equipment used to collect the data.

These particular events were considered as anomalous as triple digit rainfall totals were


47

recorded within a 6-minute period with no other rainfall recorded within several hours before

or after the event (see also Anderson, 2012; Mongwa, 2012; Nel et al., 2012; 2013).

3.4. Objective One: Identification of the top twenty events

3.4.1. Identifying an erosive event

Rainfall data in this project were separated into erosive events to calculate rainfall

erosivity (see also Anderson, 2012; Mongwa, 2012; Nel et al., 2012; 2013). The definition of

an erosive event has been well recognized within the literature. Parameters were established

for this project in order to determine an erosive event. Wischmeier & Smith (1958) specified

that for substantial amounts of soil erosion to occur rainfall intensities larger than 25mm/h are

required. Stocking & Elwell (1976) categorised a distinct erosive event when the total rainfall

exceeds 12.5mm within a 30 minute period, with a maximum 5-minute intensity exceeding

25mm/h and the event isolated by a 2 hour rain free period between erosive events. This

definition was used by Nel (2007), Nel & Sumner (2007) and Nel et al. (2010) to identify

erosive events in the KwaZulu Natal, Drakensberg of South Africa.

As rainfall on Mauritius is logged every 6 minutes the definition by Stocking & Elwell

(1976) was adjusted so that rainfall events are considered to have the potential to erode soil

on Mauritius when the total rainfall exceeds 12.5mm within a 30 minute period, maximum 6-

minute intensity exceeding 30mm/h and is isolated by at least a 2 hour rain free period (see

also Nel et al, 2012; 2013). An event was also classified as being erosive if 6.3mm of rain

occurred within 15 minutes (Wischmeier & Smith, 1978; Diodato, 2005; Angulo-Martínez &

Beguería, 2009). The definitions have also been used in numerous studies including Nel et

al. (2012), Anderson (2012); Mongwa (2012) and Nel et al. (2013) to identify erosive events

in Mauritius. After applying this definition, the data series from the 6 automated weather

stations used in this study, over the five year period, contained 444 erosive rainfall events.

3.4.2. Determining rainfall event kinetic energy

Rainfall intensity can be measured directly, however measurements of kinetic energy

and raindrop sizes are often unavailable, hence the reliance on the empirical relationships

between rainfall intensity and kinetic energy (Nel & Sumner, 2007). A number of equations

have been established to calculate kinetic energy from rainfall intensity (van Dijk et al., 2002;

Nel et al., 2012; Nel et al., 2013). Wischmeier & Smith (1958) used measurements of drop

size characteristics as well as terminal velocity to derive the relationship between rainfall


48

intensity and kinetic energy. The relationship suggested by Wischmeier & Smith is a

logarithmic function in the form:

𝐾𝐸 = 11.87 + 8.73𝐿𝑜𝑔10𝑅 where the intensity is in mm/h. (1)

Studies in Zimbabwe done by Elwell & Stocking (1973) have shown that for

subtropical climates the kinetic energy of rainfall can be predicted by the following equation:

𝐾𝐸 = (29.82 − 127.51/𝐼) in J m-2 m-1 where the intensity is in mm/h. (2)

This equation was also used in the SLEMSA (Soil Loss Estimation for Southern

Africa). Following a critical appraisal of the literature established on the relationship rainfall

intensity-kinetic energy (R – KE), based on average parameter values which were derived

from the best datasets, van Dijk et al. (2002) provided the following equation to predict storm

kinetic energy content from rainfall intensity data:

𝐾𝐸 = 28.3[1 – 0.52 𝑒𝑥𝑝 ( − .042 𝑅) where the intensity R is in mm/h. (3)

Consequently, for comparison with other studies on Mauritius, either of the

abovementioned equations would be sufficient for estimating the kinetic energy contents. In

order to provide for consistency with global studies, the equation (3) by van Dijk et al. (2002)

was used here to assess the 6-minute incremental kinetic energy content derived from

rainfall intensity.

A uniform drop size distribution is assumed during the analysis of kinetic energy. In

order to calculate the total event kinetic energy (KE) produced during each individual erosive

rainfall event (J m-2), the 6-minute kinetic energy content, multiplied by the quantity of rain

(mm) falling in that specific 6 minutes to give the 6-minute kinetic energy. Each of the 6-

minute kinetic energy values generated during the event is then summed to give the total

kinetic energy during each individual event (Nel, 2007; Nel et al., 2013). Wischmeier & Smith

(1978) determined that erosivity can be determined by the product (EI30) of the total kinetic

energy (KE) of the storm multiplied by its maximum 30-minute intensity (I30). This equation

has been used both globally and in the Mauritian context as part of the Revised Universal

Soil Loss Equation (RUSLE) to assess the spatial distribution of erosivity (see also Le Roux,

2005; Le Roux et al., 2005; Nigel, 2011; Nel et al., 2012; 2013). To ensure consistency with

previous erosivity studies in Mauritius, the rainfall erosivity potential was determined by the

product (EI30) of each erosive event (J mm m-2 h-1).


49

To investigate the intra-storm changes in rainfall attributed received from an erosive

rainfall event, the 20 events with the highest ‘total kinetic energy generated’ at each station

were used for analysis, hence a total of 120 events are analysed in detail.

3.5. Objective Two: General rainfall, climatological characteristics and

weather circulation patterns associated with the top twenty erosive

events

The six automated weather stations can be placed into two broad groups based upon

their general rainfall and other climatological characteristics. In the first group are Albion and

Beaux Songes, and in the second group, are Arnaud, Trou aux Cerfs, Grand Bassin and

Monbois. In an attempt to discover the role that an elevated topography has on the intra-

storm attributes of the events, comparisons were made between the two groups. Additionally,

comparisons between the stations within in the two groups were investigated. The following

characteristics were identified for each of the top twenty erosive events at each station and

utilised to analyse the data:

― Rainfall depth (mm),

― Rainfall event duration (minutes),

― Seasonality and date of event

― Maximum Rainfall Intensity (I6) and (I30)

― Erosivity (J mm m-2 h-1)

― Kinetic Energy Content (J m-2)

― Means including storm depth, storm duration, kinetic energy content and

erosivity

There are several weather systems of varying scales and amplitudes that affect the

island’s weather due to its location and topography (Fowdur et al., 2014; Staub et al., 2014).

Unfortunately, in the absence of detailed synoptic data it was not possible to link the erosive

events directly to the exact synoptic weather systems occurring during the time of the event.

However, some assumptions could be made using the climatological information available

from the Mauritius Meteorological Services Monthly Bulletins of Climatological Summaries,

Mauritius Meteorological Services website (MMS, 2014a) as well as the Météo-France

Réunion (Météo-France, 2015) website. The table below (Table 3-2) provides a summary of

the synoptic circulations or weather systems in Mauritius according to Fowdur et al. (2014). A

representative symbol is used for each weather system or synoptic circulations. Using


50

Fowdur et al. (2014) weather systems in conjunction with synoptic data available from

Météo-France (2015) and Mauritius Meteorological Services (MMS, 2014a) websites an

attempt was made to classify the erosive events into potential weather systems. The lack of

island and event scale synoptic data was a limitation to classifying the weather systems.

However, from the data available, an effort has been made to classify the events in order to

understand the event scale synoptics.

Table 3-2: Weather Systems in Mauritius (Fowdur et al., 2014) as representative symbols applied to the erosive events

3.6. Objective Three: Intra-storm analysis of each event

The intra-storm analysis was undertaken based upon the methodology developed

and used by Nel (2007). In order to conduct the intra-storm analysis the top twenty events

with the highest ‘total kinetic energy generated’ at each station were chosen from the

available data. Rainfall kinetic energy (KE) represents the total energy available to detach

and transport soil particles (Salles et al., 2002; van Dijk et al., 2002; Salako, 2007). While Nel

(2007) utilised ten erosive events at four automated weather stations in the Drakensberg

mountains, KwaZulu Natal, South Africa, this study considered the top twenty erosive events

at 6 automated weather stations given the longer duration (2004 to 2008) of the available

data. Hence, a total of 120 erosive events were analysed in detail. For the purpose of this

study the top twenty erosive events at each station will be referred to as the erosive events

or simply the events.

Synoptic circulation or Weather System in Mauritius (Fowdur et al., 2014)

Representative symbol

Windfields over Mauritius WF

Cyclonic Activity C

Intertropical Convergence Zone ITCZ

Cold fronts CF

Anticyclones AC

Sea breezes SB

Easterly Waves Perturbations in the Lower Troposphere EW

Cloud Masses and Upper Level Lows CM

Weather system unknown U


51

3.6.1. Intra-storm distribution of rainfall depth

3.6.1.1. Storm rainfall depth and cumulative rainfall generated over time by the erosive

events

Following the equation by van Dijk et al. (2002), the above formula (3), the six-minute

kinetic energy content (KE) of each individual event at each station was calculated and the

cumulative kinetic energy as a percentage was plotted. This was done in order to identify the

distribution pattern of the cumulative total kinetic content of the rainfall over time. Following

the method established by Nel (2007), a well-defined distribution of cumulative total kinetic

content of rainfall over time was anticipated. The graph was subsequently be used to identify

the time in minutes at which the stations would have received approximately 80% of all the

potential kinetic energy content generated by the storm. This represented the maximum

(duration) threshold over which all the extreme events would be analysed in further detail.

Once that point has been identified, the six-minute increment rainfall depth of each individual

storm measured at each station was plotted for the analysis of the intra-event temporal

distribution of the rainfall and further comparative reasons.

3.6.1.2. Storm rainfall depth as a function of storm duration

To test for rainfall generation as a function of rainfall duration, the six-minute rainfall

depth of each individual event measured at each station was plotted as a percentage of

rainfall duration. Each erosive event was divided into quartiles namely: First Quartile (Q1),

Second Quartile (Q2), Third Quartile (Q3) and Fourth Quartile (Q4). Due to the high

variability in event duration, all the storms were plotted as a percentage of the rainfall

duration in order to conduct a comparative analysis between the quartiles and identify the

point during the storm when the peak rainfall was generated. The rainfall variability of each

event was also highlighted in this process.

3.6.2. Intra-storm distribution of extreme and peak rainfall intensity

3.6.2.1. Timing of the extreme rainfall intensity generated by the erosive events

Following the modifed definition of an erosive event by Stocking & Elwell (1976),

rainfall events on Mauritius have the potential to erode soil when the total rainfall exceeds

12.5mm within a 30 minute period with a maximum 6-minute intensity exceeding 30mm/h

and a 2 hour rain free interval. Thus, the 120 erosive events analysed have a maximum six-


52

minute intensity exceeding 30mm/h. To test at what stage within an erosive event the rainfall

intensity has the potential to exceed the infiltration rates and become erosive, the intra-storm

variations of the six-minute intensity exceeding 30mm/h were considered (adapted from

Stocking & Elwell, 1976).

3.6.2.2. Timing of extreme rainfall intensity as a function of storm duration

To test for the timing of the extreme rainfall intensities (above 30mm/h) as a function

of rainfall duration, all the extreme intensities of each individual event measured at each

station were plotted as a percentage of rainfall duration. Each event is divided into quartiles,

Q1 through Q4 as explained above. This was done to identify when peak rainfall intensity

was recorded. The point was considered important as Parsons & Stone (2006) established

that a constant-intensity erosive event yields lower soil loss than the varying-intensity events,

and sediment eroded from the constant-intensity event had lower clay content than that from

the varying-intensity erosive events. Moore (1979) indicated that under natural event

conditions the peak sediment transportation coincides with the peaks in rainfall intensities.

Stocking & Elwell (1976) also found that the magnitude of peak intensities is most critical to

the erosion process. A study by Parsons & Stone (2006) found that events which had their

peak intensities towards the end of the event duration had the highest peaks in runoff rates,

soil loss and displaced the largest particle size in the eroded soil and therefore linear

relationship between rainfall intensity and runoff is generally assumed.

3.7. Objective Four: Contrast of spatial and temporal differences between

the automated weather stations

As a result of the topography of Mauritius, well-defined spatial trends in relation to

rainfall across Mauritius are acknowledged in previous studies (Dhurmea et al., 2009; Nel et

al., 2012; 2013; Staub et al., 2014). Associated with the topographic uplift and other factors is

the steep rainfall gradient and distinct west coast rainshadow effect, thus influencing the

spatial and temporal rainfall variability present on the island. The six automated weather

stations used in the project have been placed into two broad groups based upon their

general rainfall, erosive and other climatological characteristics identified in objective two. In

the first group are Albion and Beaux Songes within the low altitude region, and in the second

group, are Arnaud, Monbois, Grand Bassin and Trou aux Cerfs within the high altitude

region. While no other station data were available, these automated weather stations


53

represent the regions of the island which receive the highest and lowest rainfall totals. To

discover the role that an elevated topography has on the intra-storm attributes of the erosive

events, an attempt will be made to contrast the spatial and temporal differences between the

automated rainfall stations of the two groups. Concurrent events identified in objective two

will also be discussed.

In summary, this chapter presented the methodology followed when analysing the

high resolution automatic rainfall data provided by the Mauritius Meteorological Service along

with the instrumentation utilised in the collection of the data for this project. The procedures

undertaken in calculating and analysing the information to reach each objective set out in the

beginning of this project were also discussed. The formulae necessary for this project was

also provided and explained. The results of the data analysis will be presented in the next

chapter.


54

Chapter 4 : Results

The following chapter presents the results from the data analysis performed. The

results component is comprised of four main sections, first the general storm and rainfall data

as well as the general climatological characteristics of the top twenty erosive events (based on

‘total kinetic energy generated’) at each automated weather station are presented. Second, the

intra-storm distribution of rainfall depth was analysed by identifying the storm depth generated

over time by the erosive events as well as the storm rainfall depth as a function of storm

duration at the respective station in Mauritius. Third, the intra-storm distribution of the extreme

and peak rainfall intensity was analysed through the timing of the extreme rainfall intensity

(above 30mm/h) generated by the erosive events. The timing of the extreme rainfall intensity

(above 30mm/h) as a function of storm duration at the respective stations is also analysed.

Finally, the climatic drivers and temporal analysis of the events are presented.

4.1. General storm and rainfall data and general climatological characteristics

As rainfall data used in this project are logged every 6-minutes, the definition by

Stocking & Elwell (1976) was adjusted accordingly, so that erosive events have a total rainfall

exceeding 12.5mm within a 30 minute period with a maximum 6-minute intensity exceeding

30mm/h and a 2 hour rain free interval (see also Nel et al., 2012; 2013) or if 6.3mm of rain

occurred within 15 minutes (Wischmeier & Smith, 1978; Diodato, 2005; Angulo-Martínez &

Beguería, 2009). After applying this definition, the data series from the six automated weather

stations, over the five year period, contained a total of 444 erosive rainfall events. Of the 444

total erosive events, the low altitude west coast station recorded 42 events at Albion

(12m.a.s.l), and 46 events at Beaux Songes (225m.a.s.l). Whilst on the central plateau, 116

events at Arnaud (576m.a.s.l), 77 events were identified at Monbois (590m.a.s.l), 104 events

at Grand Bassin (605m.a.s.l) and 59 events at Trou aux Cerfs (614m.a.s.l), noticeably fewer

than the other high altitude stations on the Central Plateau.

The events (ranked highest to lowest ‘total kinetic energy generated’) at the west coast

stations of Albion and Beaux Songes represent 48% and 43% of the total erosive events that

took place at these stations respectively. Therefore, approximately half of all the events

measured at these low altitude stations are erosive in nature. However, the top twenty erosive

events at the Arnaud and Grand Bassin stations only account for 17% and 19% of the total


55

erosive events respectively. Consequently, a higher proportion of the rain at the coast stations

is considered highly erosive and only a correspondingly smaller percentage at the high altitude

stations situated on the elevated central plateau of Mauritius.

Although Nel (2007) used ten erosive events at four automated weather stations in the

Drakensberg, KwaZulu Natal, South Africa to perform the intra-storm analysis, this study

considered the twenty erosive events at each automated weather station given the longer

duration (2004 to 2008) of the available data. The twenty events with the highest ‘total kinetic

energy generated’ at each station were selected to conduct the intra-storm analysis, therefore

a total of 120 erosive events were analysed in detail (Table 4-1). For the purpose of this study

the top twenty erosive events at the six automated weather stations, referred to as the erosive

events or simply the events, are ranked according to highest ‘total kinetic energy generated’

(highest to lowest total kinetic energy generated) see Table 4-2 to Table 4-7.

The 120 erosive events (Figure 4-1) analysed in this study had an average duration of

1451 minutes (24 hours and 18 minutes) with the longest event of 4932 minutes (82 hours)

happening at the Monbois automated weather station (Table 4-5; event no. 8) and the shortest

event of 36 minutes occurring at Trou aux Cerfs (Table 4-7; event no. 11). The mean rainfall

depth of the events is 117.04mm, with the lowest rainfall depth totalling 27.2mm at Albion

(Table 4-2; event no. 20) and the highest rainfall depth equalling 615mm at Trou aux Cerfs

(Table 4-7; event no. 1).

In Figure 4-1, 87.5% of the 120 events have a total storm duration of less than 3000

minutes and 95% of the events have a rainfall depth of less than 300mm. The outlying events

in Figure 4-1 can be attributed to cyclonic activity, with the exception of the outlying point with

the high rainfall depth and very short rainfall duration which corresponds to event no. 1 at the

Trou aux Cerfs automated weather station. The total rainfall depth (Table 4-1) generated by

the 120 events was 14045.2mm, with the automated weather station of Grand Bassin (Table 4-

6) contributing the most erosive rainfall 3222.2mm (23%), followed by the automated weather

station at Arnaud (Table 4-4) that contributed 3006.8mm (21.5%), then Trou aux Cerfs weather

station 2808mm (20%), followed by the weather station at Monbois 2304.6mm (16.5%), and

lastly the coastal stations of Albion 1406.4mm (10%) and Beaux Songes 1297.2mm (9%)

(Table 4-3).


56

Figure 4-1: Mean rainfall depth and duration of the 120 erosive events analysed in this study

When considering the Total Kinetic Energy (KE) (Table 4-1) and Total Rainfall erosivity

(EI30) (Table 4-1) across all six automated weather stations, the 120 events received a total

kinetic energy of 250329 (J m-2) and a total rainfall erosivity of 11255814 (J mm m-2 h-1). The

coastal station of Beaux Songes (Table 4-3) has the lowest total KE 23192 (J m-2) which

accounts for only 9.2% of the total KE, and lowest total erosivity of 810306 (J mm m -2 h-1),

which represents only 7.2% of the total rainfall erosivity across all six automated weather

stations.

Table 4-1: Total intra-storm attributes at the six automated weather stations

Automated weather stations

Total storm depth (mm)

Total storm duration (minutes)

Total kinetic energy (J m

-2)

Total erosivity (J mm m

-2 h

-1)

Albion (12m.a.s.l) 1406.4 14868 25356 905812

Beaux Songes (225m.a.s.l) 1297.2 14004 23192 810306

Arnaud (576m.a.s.l) 3006.8 38022 51705 1827222

Monbois (590m.a.s.l) 2304.6 35808 40034 1439542

Grand Bassin (605m.a.s.l) 3222.2 47808 54007 1739853

Trou aux Cerfs (614m.a.s.l)

2808 23611 56035 4533079

Total 14045.2 174121 250329 11255814

0

50

100

150

200

250

300

350

400

450

500

550

600

650

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500

Rai

nfa

ll d

ep

th (m

m)

Duration (minutes)

Mean rainfall depth

Mean rainfall duration


57

The events at the Albion (Table 4-2) automated weather station account for 8% of the total

rainfall erosivity and 10.1% of the total KE across all six stations. Although the automated

weather station at Trou aux Cerfs receives noticeably fewer total erosive events than any of

the other stations on the central plateau, the top twenty events at this weather station have the

highest ‘total kinetic energy content’ generated 56035 (J m-2), representing 22.4% of the total

KE across all 6 six automated weather stations- with event no. 1 at Trou aux Cerfs contributing

more than 26% of the total KE (Table 4-7).

At Albion, Beaux Songes and Grand Bassin (Table 4-6) the corresponding event no. 1

contributes at each station represents 11.9%, 11.5% and 11.7% respectively. The high altitude

station of Trou aux Cerfs also has highest total erosivity 4533079 (J mm m-2 h-1) (Table 4-1) of

all six automated weather stations, and represents 40.3% of the total rainfall erosivity across

all six stations. Event no. 1 at Trou aux Cerfs is responsible for 49% of the total rainfall

erosivity at this automated weather station. This is more than double the rainfall erosivity

received at any other station during their corresponding events no. 1. For example, at the low

altitude station of Albion and the inland station of Monbois, the comparable event no. 1 (at

each corresponding station) is responsible for only 8% of the rainfall erosivity at these stations.

While, the rainfall erosivity received during the event no. 1 at the automated weather station of

Beaux Songes is 19%.

The maximum six-minute rainfall intensity (I6) of the events measured at the six

automated weather stations ranged from 26 to 198mm/h. Highest maximum six-minute rainfall

intensity (I6) of 198 mm/h was measured at two automated weather stations, namely Trou aux

Cerfs (Table 4-7) and Monbois (Table 4-5). The maximum (I6) values measured were 116

mm/h at Albion (Table 4-2) and Beaux Songes (Table 4-3) and 102mm/h at Arnaud (Table 4-

4). The lowest maximum six-minute rainfall intensity (I6) of 26mm/h was measured at Grand

Bassin (Table 4-6). The maximum 30-minute rainfall intensity (I30) ranged from 13.2 to

152mm/h. The maximum I30 value was recorded at Trou aux Cerfs (the highest station).


58

Table 4-2: The top twenty erosive event attributes for Albion (12m.a.s.l) from 2004 to 2008

Event number

Date

and

Time

Start

Date

and

Time

End

Storm

Depth

(mm)

Storm

Duration

(minutes)

Max

Intensity

(I6)

(mm/h)

Max

Intensit

y

(I30)

(mm/h)

Total

Kinetic

Energy

(J m-2

)

Erosivity

(J mm m-2

h-1

)

Synoptic

circulation

or Weather

System

1 16/09/08 (17:18)

17/09/08 (20:30)

177.6 1638 42 24 3015 72350 U

2 04/03/05 (19:24)

05/03/05 (07:30)

117.4 762 88 46 2127 97852 U

3 03/03/06 (22:30)

05/03/06 (05:54)

119.0 1866 26 17 1857 31945 C

4 09/06/07 (10:18)

10/06/07 (02:18)

96.0 966 50 30 1667 50683 U

5 30/11/08

(10:24)

30/11/08

(13:12) 74.2 138 48 46 1599 73543 U

6 23/01/06

(22:18)

24/01/06

(06:24) 83.2 498 66 40 1584 63994 U

7 25/03/08

(13:24)

26/03/08

(10:54) 97.8 1302 30 20 1548 30954 C

8 08/03/04

(07:24)

08/03/04

(09:18) 59.0 90 116 92 1425 131136 U

9 22/03/05

(18:30)

24/03/05

(14:24) 81.0 2682 54 18 1245 22918 C

10 02/01/06

(03:06)

02/01/06

(08:24) 63.8 354 70 38 1178 44764 C

11 16/02/06

(17:18)

17/02/06

(07:54) 63.6 858 38 32 1111 35557 C

12 02/03/05

(14:12)

02/03/05

(17:30) 50.6 204 82 46 1049 48674 U

13 13/12/04

(18:48)

14/12/04

(03:24) 59.6 516 42 24 989 23746 U

14 20/03/05

(19:12)

21/03/05

(07:06) 55.6 690 26 16 906 14853 C

15 23/04/05

(11:30)

23/04/05

(12:30) 34.0 66 100 65 818 52979 U

16 22/02/08

(14:24)

23/02/08

(07:24) 46.6 1026 38 23 756 17239 C

17 14/05/08

(08:42)

14/05/08

(14:36) 34.8 360 80 50 727 36058 CF

18 22/10/07

(06:42)

22/10/07

(15:42) 36.0 546 92 38 673 25292 SB

19 12/01/04

(13:30)

12/01/04

(15:18) 29.4 108 46 28 581 16503 U

20 13/01/04

(13:24)

13/01/04

(16:42) 27.2 198 54 30 499 14769 U

TOTAL 1406.4 14868 TOTAL 25356 905812

MEAN 70.3 743 MEAN 1268 45291

WF= Windfields over Mauritius, C= Cyclonic Activity, ITCZ=Intertropical Convergence Zone, CF= Cold fronts, AC= Anticyclones, SB= Sea breezes, EW=

Easterly Waves Perturbations in the Lower Troposphere, CM= Cloud Masses and Upper Level Lows, U= Weather system unknown. See Table 3-3.


59

Table 4-3: The top twenty erosive event attributes for Beaux Songes (225 m.a.s.l) from 2004 to 2008

Event

number

Date

and

Time

Start

Date

and

Time

End

Storm

Depth

(mm)

Storm

Duration

(minutes)

Max

Intensity

(I6)

(mm/h)

Max

Intensity

(I30)

(mm/h)

Total

Kinetic

Energy

(J m-2

)

Erosivity

(J mm m-2

h-1

)

Synoptic

circulation

or Weather

System

1 04/03/05

(19:24)

05/03/05

(07:24) 138.0 726 94 57 2673 151849 U

2 25/03/08

(13:24)

26/03/08

(11:06) 130.4 1320 50 39 2230 87417 C

3 23/01/06

(17:54)

24/01/06

(06:42) 108.2 768 60 36 1957 70450 U

4 16/02/06

(17:18)

17/02/06

(07:54) 89.8 888 58 43 1693 73130 C

5 03/03/06

(22:12)

04/03/06

(20:42) 103.6 1362 40 14 1634 22220 C

6 15/03/08

(03:30)

15/03/08

(06:42) 57.8 192 86 51 1240 63482 C

7 22/03/05

(18:42)

24/03/05

(08:42) 74.0 2286 30 14 1108 15063 C

8 01/02/04

(08:18)

01/02/04

(12:36) 54.8 270 54 36 1030 37506 C

9 09/06/07

(09:30)

09/06/07

(23:30) 61.6 840 32 16 988 15814 U

10 04/02/05

(11:24)

04/02/05

(14:42) 47.4 198 116 52 985 51215 C

11 12/01/04

(12:36)

12/01/04

(15:12) 48.4 150 64 34 968 33291 U

12 27/01/04

(08:24)

27/01/04

(10:30) 38.4 132 102 59 861 50622 C

13 04/03/04

(11:24)

05/03/04

(04:24) 47.2 990 62 23 799 18221 C

14 14/02/04

(07:24)

14/02/04

(11:12) 39.4 240 66 36 776 27951 U

15 15/02/05

(20:48)

16/02/05

(02:30) 41.4 342 42 29 750 21615 C

16 22/01/05

(15:06)

23/01/05

(02:24) 42.6 684 48 22 738 16236 C

17 22/10/07

(07:06)

22/10/07

(13:42) 40.2 408 80 28 719 20409 SB

18 20/03/05

19:42)

21/03/05

(07:12) 44.6 684 34 15 703 10960 C

19 19/09/05

07:24)

19/09/05

(19:36) 48.2 780 32 18 698 12839 U

20 22/02/08

(14:18)

23/02/08

(02:36) 41.2 744 42 16 642 10015 C

TOTAL 1297.2 14004 TOTAL 23192 810306

MEAN 64.86 700 MEAN 1160 40515




60

Table 4-4: The top twenty event attributes for Arnaud (576 m.a.s.l) from 2004 to 2008

Event

number

Date

and

Time

Start

Date

and

Time

End

Storm

Depth

(mm)

Storm

Duration

(minutes)

Max

Intensity

(I6)

(mm/h)

Max

Intensity

(I30)

(mm/h)

Total

Kinetic

Energy

(J m-2

)

Erosivity

(J mm m-2

h-1

)

Synoptic

circulation

or Weather

System

1 22/03/05

(08:48)

25/03/05

(04:06) 435.8 4014 68 24 7293 177954 C

2 02/03/06

(18:24)

05/03/06

(22:48) 301.2 4560 44 23 4808 111543 C

3 16/05/08

(04:30)

17/05/08

(21:42) 255.4 2478 84 53 4469 235963 CF

4 24/03/08

(23:36)

26/03/08

(15:36) 299.8 2376 70 42 4108 170877 C

5 18/03/05

(02:06)

19/03/05

(11:42) 223.0 2022 80 44 4020 178501 C

6 23/01/06

(16:12)

24/01/06

(09:30) 141.0 1044 56 25 2422 60059 U

7 30/04/04

(16:42)

02/05/04

(16:12) 154.2 2856 30 17 2338 39274 U

8 04/03/05

(15:00)

05/03/05

(19:54) 128.0 1734 82 45 2213 100005 U

9 01/01/04

(16:54)

03/01/04

(07:06) 129.4 2292 38 13 1994 26319 C

10 16/02/06

(13:42)

17/02/06

(04:48) 103.8 918 66 33 1966 65274 C

11 09/04/04

(08:30)

10/04/04

(10:12) 112.2 1548 74 40 1904 75401 U

12 28/11/08

(20:06)

01/12/08

(04:06) 109.4 2826 60 29 1767 50883 U

13 20/03/05

(20:36)

22/03/05

(06:52) 106.2 2064 58 32 1714 55540 C

14

26/02/04

(05:30)

26/02/04

(15:54) 84.0 630 82 54 1702 92596 C

15 31/01/04

(12:24)

01/02/04

(13:18) 88.2 1512 64 40 1561 63051 C

16 01/01/06

(11:18)

03/01/06

(04:18) 98.0 2472 56 19 1555 29858 C

17 20/03/04

(00:24)

21/03/04

(19:42) 86.6 1158 94 46 1529 70952 U

18 12/01/04

(10:36)

12/01/04

(15:00) 69.8 270 92 54 1474 79619 U

19 22/02/08

(13:00)

23/02/08

(06:36) 89.0 1068 40 19 1460 28026 C

20 16/03/08

(10:18)

16/03/08

(13:12) 61.6 180 102 82 1409 115529 C

TOTAL 3006.8 38022 TOTAL 51705 1827222

MEAN 150.3 1901 MEAN 2585 91361




61

Table 4-5: The top twenty erosive event attributes for Monbois (590m.a.s.l) from 2004 to 2008

Event

number

Date

and

Time

Start

Date

and

Time

End

Storm

Depth

(mm)

Storm

Duration

(minutes)

Max

Intensity

(I6)

(mm/h)

Max

Intensity

(I30)

(mm/h)

Total

Kinetic

Energy

(J m-2

)

Erosivity

(J mm m-2

h-1

)

Synoptic

circulation or

Weather

System

1 22/03/05

(13:48)

25/03/05

(04:18) 310.2 3756 36 22 4984 109648 C

2 16/02/06

(16:24)

17/02/06

(19:52) 156.2 1656 86 45 2943 131829 C

3 28/11/08

(13:12)

30/11/08

(11:30) 152.4 2754 70 45 2850 128822 U

4 04/03/06

(07:42)

05/03/06

(23:06) 161.2 2370 198 69 2827 195617 C

5 18/03/05

(00:42)

19/03/05

(17:30) 152.4 2454 76 37 2569 94544 C

6 23/01/06

(15:52)

24/01/06

(11:36) 144.8 1188 54 26 2484 64572 U

7 12/01/04

(10:18)

12/01/04

(15:00) 110.6 288 96 74 2364 173974 U

8 08/04/04

(16:30)

12/04/04

(03:06) 143.0 4932 48 24 2196 51833 U

9 01/05/04

(00:36)

02/05/04

(15:30) 133.4 2334 30 14 2014 28192 U

10 05/07/05

(22:42)

07/07/05

(06:36) 117.4 1914 26 20 1858 36416 U

11 04/03/05

(19:18)

06/03/05

12:30 112.0 2478 58 21 177 37679 U

12 04/03/04

(08:48)

05/03/04

(22:42) 105.6 2280 46 17 1623 27917 C

13 01/03/04

(08:48)

01/03/04

(11:30) 70.6 168 96 65 1620 105636 C

14 14/02/05

(07:00)

16/02/05

(02:42) 101.0 2628 36 16 1543 24693 C

15 31/01/04

(11:48)

01/02/04

(13:18) 70.2 1536 66 34 1202 40388 C

16 28/12/08

(09:52)

28/12/08

(11:48) 48.0 120 46 32 1187 37970 U

17 31/12/05

(14:24)

01/01/06

(05:54) 59.4 906 54 29 1014 29216 C

18 18/09/08

(08:06)

18/09/08

(17:18) 42.8 558 178 72 999 71899 U

19 27/03/04

(21:36)

28/03/04

(19:30) 60.8 1320 46 21 993 20649 U

20 29/02/04

(08:30)

29/02/04

(11:12) 52.6 168 58 28 988 28048 U

TOTAL 2304.6 35808 TOTAL 40034 1439542

MEAN 115.23 1790 MEAN 2002 71977




62

Table 4-6: The top twenty erosive event attributes for Grand Bassin (605m.a.s.l) from 2004 to 2008

Event

number

Date

and

Time

Start

Date

and

Time

End

Storm

Depth

(mm)

Storm

Duration

(minutes)

Max

Intensity

(I6)

(mm/h)

Max

Intensity

(I30)

(mm/h)

Total

Kinetic

Energy

(J m-2

)

Erosivity

(J mm m-2

h-1

)

Synoptic

circulation

or Weather

System

1 22/03/05

(09:24)

25/03/05

(02:42) 384.8 3924 52 31 6334 197612 C

2 18/03/05

(02:00)

20/03/05

(10:24) 247.6 3390 68 36 4138 147300 C

3 16/05/08

(07:00)

17/05/08

(20:48) 209.2 2298 120 52 3657 188688 CF

4 15/09/08

(18:30)

18/09/08

(10:00) 229.4 3810 28 14 3509 49126 U

5 24/03/08

(08:12)

26/03/08

(15:18) 218.4 3372 42 26 3479 90454 C

6 30/04/04

(15:30)

02/05/04

(15:48) 204.8 2904 38 14 3172 44415 U

7 05/07/05

(00:24)

07/07/05

(07:12) 187.6 1854 28 18 3056 53784 U

8 03/03/06

(19:24)

05/03/06

(22:42) 171.4 3126 42 22 2713 58605 C

9 23/01/06

(17:42)

24/01/06

(09:42) 148.8 966 86 39 2599 100833 U

10 09/04/04

(09:06)

11/04/04

(06:24) 145.2 2724 58 35 2539 89374 U

11 14/02/08

(11:42)

17/02/08

(04:12) 165.6 3876 34 16 2511 37165 C

12 14/03/08

(10:30)

15/03/08

(07:30) 106.2 1272 102 58 2188 127757 C

13 04/03/05

(15:12)

06/03/05

(11:48) 127.0 2688 118 82 2125 175135 U

14 01/01/04

(15:12)

03/01/04

(08:52) 129.4 2538 56 31 2099 64640 C

15 26/02/04

(06:12)

26/02/04

(17:30) 93.8 660 48 18 1974 34735 U

16 22/01/05

(09:58)

23/01/05

(15:12) 98.4 1758 54 26 1619 41443 C

17 29/11/08

(00:18)

01/12/08

(03:36) 105.6 3054 36 19 1601 30106 U

18 09/04/05

(15:30)

11/04/05

(18:30) 103.8 3060 26 19 1573 30202 C

19 08/01/05

(07:36)

08/01/05

(14:42) 78.4 432 74 46 1566 72679 U

20 16/03/08

(10:18)

16/03/08

(11:48) 66.8 102 122 68 1556 105802 C

TOTAL 3222.2 47808 TOTAL 54007 1739853

MEAN 161.11 2390 MEAN 2700 86993




63

Table 4-7: The top twenty erosive events attributes for Trou aux Cerfs (614m.a.s.l) from 2004 to 2008

Event

number

Date

and

Time

Start

Date

and

Time

End

Storm

Depth

(mm)

Storm

Duration

(minutes)

Max

Intensity

(I6)

(mm/h)

Max

Intensity

(I30)

(mm/h)

Total

Kinetic

Energy

(J m-2

)

Erosivity

(J mm m-2

h-1

)

Synoptic

circulation or

Weather

System

1 28/05/06

(10:18)

29/05/06

(03:30) 615.0 1038 198 152 14738 2240158 U

2 26/05/06

(22:12)

27/05/06

(11:36) 250.6 816 198 138 5896 816062 U

3 22/03/05

(14:48)

25/03/05

(04:24) 322.8 3696 56 30 5362 158710 C

4 02/03/06

(17:30)

05/03/06

(06:18) 238.4 3645 34 20 3756 73618 C

5 23/01/06

(16:12)

24/01/06

(11:24) 175.0 1158 74 38 3165 121544 U

6 09/04/04

(09:06)

12/04/04

(04:24) 133.2 3972 50 28 2178 60980 U

7 25/01/04

(13:58)

25/01/04

(16:42) 90.4 168 118 78 2139 167729 C

8 04/03/04

(08:54)

05/03/04

(13:30) 129.0 1716 82 28 2076 57303 C

9 18/03/05

(04:54)

19/03/05

(05:18) 113.8 1464 92 42 2066 86770 C

10 02/03/04

(08:58)

02/03/04

(11:06) 90.0 186 102 53 1973 104178 C

11 03/01/06

(06:58)

03/01/06

(07:30) 68.0 36 198 133 1845 245782 C

12 29/02/04

(08:30)

29/02/04

(11:36) 85.2 186 102 53 1843 97301 U

13 01/01/04

(16:24)

03/01/04

(03:42) 89.6 2124 46 15 1342 19864 C

14 28/05/06

(01:24)

28/05/06

(06:06) 66.4 288 198 46 1328 61636 U

15 31/01/04

(20:42)

01/02/04

(12:36) 70.6 954 76 32 1294 41919 C

16 19/02/05

(07:54)

19/02/05

(14:42) 60.2 384 58 37 1153 42881 U

17 13/01/04

(09:00)

13/01/04

(15:48) 56.4 414 68 39 1069 41893 U

18 13/12/04

(18:06)

14/12/04

(05:42) 60.0 708 40 19 980 18816 U

19 13/04/04

(11:24)

13/04/04

(13:48) 43.0 150 98 52 921 48260 U

20 03/02/08

(14:00)

03/02/08

(22:00) 50.4 498 64 30 910 27676 C

TOTAL 2808 23611 TOTAL 56035 4533079

MEAN 140.4 1181 MEAN 2802 226654




64

4.2. Intra-storm distribution of rainfall depth

4.2.1. Storm rainfall depth and cumulative rainfall generated over time by the erosive

events

Following the equation by van Dijk et al. (2002), the six-minute kinetic energy content

(KE) of each individual event at each automated weather station was calculated and the

cumulative kinetic energy as a percentage was plotted in (Figure 4 – 2). The top twenty

events at the six automated weather stations exhibit a well-defined exponential distribution of

cumulative kinetic energy of rainfall over time (storm duration). Events at Beaux Songes and

Albion automated weather stations generate 90% of the total kinetic energy in the first 1500

minutes of the event. At the automated weather station of Arnaud 85% of the total cumulative

kinetic energy is received within the first 2000 minutes of the event. The events at Trou aux

Cerfs and Grand Bassin generate 90% of the total cumulative kinetic energy in the first 2500

minutes from the onset of the event. At the automated weather station of Monbois 85% of the

total cumulative kinetic energy is received within the first 2500 minutes of the event.

Subsequently, 93% of all the events analysed in this project receive 80% of the total

cumulative kinetic energy within the first 2500 minutes of the event. The extremely long

storm duration could be the caused by a long lag in the fourth quartile (Q4) of events.

Despite the 120 events all displaying clear variations in rainfall depth over time, all the

stations received more than 80% of the cumulative kinetic energy content within the first

2500 minutes of the event. Therefore, the six-minute increment rainfall depth of each

individual event measured at each station was plotted over the first 2500 minutes in order to

analyse the intra-event temporal distribution of rainfall. At the low altitude station of Albion

75% of the cumulative rainfall received at this station occurs within the first 702 minutes from

the onset of the event, with most of the events occurring at this station tapering out before

reaching 1500 minutes with only three storms exceeding this time (Figure 4-3. A). Two of the

three events that exceed the 1500 minutes are both associated with cyclonic activity in the

vicinity of the island, namely Tropical Cyclone Diwa and Tropical Cyclone Hennie.


65

At the low altitude station of Beaux Songes 75% of the cumulative rainfall occurs

within the first 600 minutes from the onset of the event. From Figure 4-3. B it is evident that

most of the events taper out before 1500 minutes with only one event exceeding this

duration. The events at automated weather station of Beaux Songes (event no. 7; Table 4-3)

that exceeded 1500minutes were associated with rainfall received from Cyclone Hennie. At

the inland automated weather station of Arnaud (Figure 4-4. C) and the high altitude station

of Grand Bassin (Figure 4-5. E) 65% of the cumulative rainfall is received within the first 1704

and 1920 minutes respectively. While at the automated weather station of Monbois (Figure

4-4. D) and high altitude station of Trou aux Cerfs (Figure 4-5. F) 70% of the cumulative

rainfall is received within the first 1548 and 984 minutes. The events at Trou aux Cerfs

started tapering out after 1500 minutes. Evidently, visible tapering off of the events occur at

most of the stations, excluding Grand Bassin, where despite longer events occurring, 80% of

the cumulative rainfall is received within the first 2500 minutes from the onset of the event.

0

10

20

30

40

50

60

70

80

90

100

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Cu

mu

lati

ve K

inet

ic E

ner

gy (%

)

Time (minutes)

A: Albion (Coastal)

B: Beaux Songes(Coastal)

C: Arnaud (CentralPlateau)

D: Monbois (CentralPlateau)

E: Grand Bassin(Central Plateau)

F: Trou aux Cerfs(Central Plateau)

E

C

D

F A

B

Figure 4-2: Cumulative kinetic energy generated over time by the erosive events at the respective automated weather stations on Mauritius


66

0

5

10

15

20

25

0 500 1000 1500 2000 2500

Rai

nfa

ll d

epth

(m

m)

Time (minutes )

A

0

20

40

60

80

100

0 500 1000 1500 2000 2500

Cu

mu

lati

ve r

ain

fall

(%)

Time (minutes)

0

5

10

15

20

25

0 500 1000 1500 2000 2500

Rai

nfa

ll d

epth

(mm

)

Time (minutes)

0

20

40

60

80

100

0 500 1000 1500 2000 2500

Cu

mu

lati

ve r

ain

fall

(%)

Time (minutes)

B

Figure 4-3: Rainfall depth and cumulative rainfall generated over time by the events measured at the respective stations on Mauritius: (A) Albion and (B) Beaux Songes

66


67

0

5

10

15

20

25

0 500 1000 1500 2000 2500

Rai

nfa

ll d

ep

th (m

m)

Time (minutes)

0

20

40

60

80

100

0 500 1000 1500 2000 2500

Cu

mu

lati

ve r

ain

fall

(%)

Time (minutes)

0

5

10

15

20

25

0 500 1000 1500 2000 2500

Rai

nfa

ll d

epth

(mm

)

Time (minutes)

0

20

40

60

80

100

0 500 1000 1500 2000 2500

Cu

mu

lati

ve r

ain

fall

(%)

Time (minutes)

C

D

Figure 4-4: Rainfall depth and cumulative rainfall generated over time by the events measured at the respective stations on Mauritius: (C) Arnaud and (D) Monbois

67


68

0

5

10

15

20

25

0 500 1000 1500 2000 2500

Rai

nfa

ll d

ep

th (m

m)

Time (minutes)

0

20

40

60

80

100

0 500 1000 1500 2000 2500Cu

mu

lati

ve r

ain

fall

(%)

Time (minutes)

0

5

10

15

20

25

0 500 1000 1500 2000 2500

Rai

nfa

ll d

epth

(mm

)

Time (minutes)

0

50

100

0 500 1000 1500 2000 2500

Cu

mu

lati

ve

rain

fall

(%)

Time (minutes)

Figure 4-5: Rainfall depth and cumulative rainfall generated over time by the events measured at the respective stations on Mauritius: (E) Grand Bassin and (F) Trou aux Cerfs

F

E

68


69

4.2.2. Storm rainfall depth as a function of storm duration

To test the rainfall generation as a function of rainfall duration, the six-minute rainfall

depth of each individual event was plotted as a percentage of rainfall duration and then each

event was divided into quartiles, Q1 through Q4 (Figure 4-6). As anticipated the top twenty

events at the six automated weather station indicate variability of rainfall over time, but at the

low altitude stations (Albion and Beaux Songes) a high proportion of the events generate

peak rainfall within the first (Q1) and second (Q2) quartiles or the first half (Q1 and Q2) of the

event. At the low altitude stations of Albion (Figure 4 – 6. A) and Beaux Songes Figure 4 – 6.

B), 70% and 65% of the rainfall is received during the first half (Q1 and Q2) of the event,

whilst only 30% and 35% rainfall is received in the second half (Q3 and Q4) of the event.

The events at Arnaud (Figure 4 – 6. C) and Grand Bassin (Figure 4 – 6. E) are fairly

evenly distributed, with 55% of the peak rainfall generated during the first half (Q1 and Q2) of

the event and 45% generated during the second half (Q3 and Q4) of the event at both

stations. At the Monbois automated weather station (Figure 4 – 6. D) a high proportion of the

erosive events (65%) generated peak rainfall during the first half, which is similar to both low

altitude stations, specifically Albion and Beaux Songes. The events experienced at the

automated weather station at Trou aux Cerfs (Figure 4 – 6. F) receives the highest proportion

of rainfall during the second (Q2) and third (Q3) quartiles with 75 % of the peak rainfall being

generated during this period. Only 5% of the peak rainfall is generated during the first quartile

(Q1), which is distinctly different from all the other stations. Hence, 80% of the peak rainfall is

generated between the first (Q1) and the third (Q3) quartile and consequently only 20% is

received during the fourth quartile (Q4). The weather station with the highest altitude Trou

aux Cerfs thus has the highest variability between the timing of rainfall.

4.3. Intra-storm distribution of extreme and peak intensity

4.3.1. Timing of extreme (above 30mm/h) rainfall intensity generated by the erosive

events

Utlising the modified definition of an erosive event by Stocking & Elwell (1976),

ensures the applicability of the six-minute rainfall data collected and guarantees consistency

with previous studies on Mauritius (see also Anderson, 2012; Mongwa, 2012; Nel et al.,

2012; 2013).


70

Figure 4-6: Rainfall depth as a function of event duration at the respective stations in Mauritius. (A) Albion; (B) Beaux Songes; (C) Arnaud; (D) Monbois; (E) Grand Bassin; (F) Trou aux Cerfs

0

5

10

15

20

25

0 25 50 75 100

Rai

nfa

ll D

ep

th (

mm

)

Duration (%)

Q1 45%

Q2 25%

Q3 25%

Q4 5%

A

0

5

10

15

20

25

0 25 50 75 100

Rai

nfa

ll D

ep

th (

mm

)

Duration (%)

Q4 20%

Q3 15%

Q2 25%

Q1 40%

B

0

5

10

15

20

25

0 25 50 75 100

Rai

nfa

ll D

epth

(m

m)

Duration (%)

Q4 15%

Q3 30%

Q2 35%

Q1 20%

C

0

5

10

15

20

25

0 25 50 75 100

Rai

nfa

ll D

epth

(m

m)

Duration (%)

Q1 30%

Q2 35%

Q3 25%

Q4 10%

D

0

5

10

15

20

25

0 25 50 75 100

Rai

nfa

ll D

epth

(m

m)

Duration (%)

Q4 10%

Q3 35%

Q2 35%

Q1 20% D

0

5

10

15

20

25

0 25 50 75 100

Rai

nfa

ll D

epth

(m

m)

Duration (%)

Q1 5%

Q3 30%

Q2 45%

Q4 20%

F

70


71

To test at which stage during an event the rainfall intensity has the potential to

exceed the infiltration rates and sebsequently become potentially erosive (using the defintion

adapted from Stocking & Elwell (1976)), the intra-storms variations of six-minute intensity

exceeding 30mm/h were considered. The timing of the extreme intensities are vital as it

influences the type and particle size of the eroded materials.

Nearly all the 120 events (75%) measured at all six automated weather stations have

intensities above 30mm/h during the first 500 minutes (Figure 4-7). The vast majority (81%)

of the 120 events receive their peak rainfall intensities within the first 1500 minutes of the

event. At the automated weather stations of Albion (Figure 4–7. A) and Beaux Songes

(Figure 4–7. B), 83% and 75% respectively of the high rainfall intensities are received within

the first 500 minutes of the event, with all (100%) of the high rainfall intensities being

received within 1500 minutes. The events received at the inland automated weather station

of Arnaud (Figure 4–7. C) display the most variability in the peak intensities, 65% of the

extreme rainfall intensities are received within the first 1000 minutes. The events measured

at the high-altitude automated weather station of Trou aux Cerfs (Figure 4–7. F) received

70% of the extreme rainfall intensities within the first 1000 minutes, some as high as

198mm/h, several of the extreme intensities were received as late as 3408 minutes from the

onset of the event. The possibility of overlapping or ‘back-to-back’ storms could be

considered.

At the inland automated weather stations of Monbois and Grand Bassin (Figure 4–7.

E), 63% and 70% respectively, of the high rainfall intensities are received within the first 1500

minutes. Although the automated weather station at Monbois (Figure 4–7. D) and at Trou aux

Cerfs (Figure 4-7 F) have a maximum intensity of 198mm/h, Trou aux Cerfs received delayed

peaks in intensity during the storm duration. These peaks are noticeably later in the event

duration than the extreme rainfall intensities received at any of the other stations. Both low

altitude stations of Albion and Beaux Songes have a maximum extreme intensity of 116mm/h

and Grand Bassin has a maximum intensity of 118mm/h. The automated weather station of

Arnaud receives the lowest maximum rainfall intensity of 102mm/h across all six of the

automated weather stations. Particularly high extreme intensities are mostly confined to the

first 1500 minutes of the events at all the stations, though some events receive peak

intensities well after the first 1500 minutes. Arnaud, Monbois, Grand Bassin and Trou aux

Cerfs all receive peak rainfall intensities well after the first 500 minutes, with intensities

peaking as late as 2000 minutes after the onset of the events. This is distinctly different at

the low altitude stations, Albion and Beaux Songes, which receive all their peak intensities

during the first 1500 minutes. The peak in extreme intensities occurring late in the event,


72

such as at the high altitude stations, could have a significant impact on the potential rainfall

erosivity experienced at these stations.

4.3.2. Extreme rainfall intensity (above 30mm/h) as a function of storm duration

When considering the distribution of the high intensity rainfall as a function of rainfall

duration, just over half (57%) of the 120 events have their high intensities within the first

(Q1) and second (Q2) quartile of rainfall duration (Figure 4-8). At both Albion (Figure 4-8. A)

and Beaux Songes (Figure 4–8. B) 40% and 45% of the events respectively, received their

rainfall intensities during the first quartile (Q1) and 68% and 65% of the peak intensity are

received during the first half of the event (Q1 and Q2). At Albion only 4% of the peak

intensities occur during the fourth quartile (Q4) whilst Beaux Songes receives only 10% of

the peak intensities during the third quartile (Q3). At the automated weather stations of

Albion and Beaux Songes only 32% and 35%, respectively, of the peak intensities occur

during the second half of the event (Q3 and Q4). The inland automated weathers of Arnaud

(Figure 4–8. C) and Monbois (Figure 4–8. D) display a similar pattern, with 60% and 61%,

respectively, occurring during the first half of the event (Q1 and (Q2).

The automated weather station at Grand Bassin displays the least amount of

variability of extreme rainfall intensity distribution, with 53% being received during the first

half (Q1 and Q2) and 48% during second half (Q3 and Q4) of the event (Figure 4–8. E).

Trou aux Cerfs (Figure 4–8. F) displays a similar pattern to the other automated weather

stations, where the majority (65%) of the peak intensity are received during the first half (Q1

and Q2) of the event, and only 35% being received during the second half (Q3 and Q4).

However, automated weather station at Trou aux Cerfs is the only rainfall station that

receives extreme high rainfall intensities (198mm/h) during the third and fourth quartiles (Q3

and Q4). This is noteworthy as it does not take place at any other automated weather

stations, where the extreme high rainfall intensities are typically confined to the first (Q1)

and second (Q2) quartiles. These peak intensities occurring so late into the event could

have major implications for the erosivity at this automated weather station.


73

Figure 4-7: Timing of extreme rainfall intensity (above 30mm/h) generated by the erosive events at the respective stations (each individual event is presented by a different symbol). (A) Albion;(B) Beaux Songes; (C) Arnaud; (D) Monbois; (E) Grand Bassin; (F) Trou aux Cerfs

0

50

100

150

200

250

0 500 1000 1500 2000 2500Rai

nfa

ll In

ten

sity

(mm

/h)

Time (minutes)

A

0

50

100

150

200

250

0 500 1000 1500 2000 2500Rai

nfa

ll In

ten

sity

(m

m/h

)

Time (minutes)

B

0

50

100

150

200

250

0 500 1000 1500 2000 2500

Rai

nfa

ll In

ten

sity

(mm

/h)

Time (minutes)

C

0

50

100

150

200

250

0 500 1000 1500 2000 2500

Ra

infa

ll In

ten

sity

(mm

/h)

Time (minutes)

D

0

50

100

150

200

250

0 500 1000 1500 2000 2500

Rai

nfa

ll In

ten

sity

(mm

/h)

Time (minutes)

E

0

50

100

150

200

250

0 500 1000 1500 2000 2500

Rai

nfa

ll In

ten

sity

(mm

/h)

Time (minutes)

F

73


74

Figure 4-8: Timing of extreme rainfall intensity (above 30mm/h) as a function of rainfall duration at the respective stations (each individual event is presented by a different symbol). (A) Albion; (B) Beaux Songes; (C) Arnaud; (D) Monbois (E) Grand Bassin; (F)Trou aux Cerfs

0

50

100

150

200

250

0 25 50 75 100

Rai

nfa

ll In

ten

sity

(mm

/h)

Duration (%)

A Q1 40%

Q2 28%

Q3 28%

Q4 4%

0

50

100

150

200

250

0 25 50 75 100

Rai

nfa

ll In

ten

sity

(mm

/h)

Duration (%)

B Q1 45%

Q2 20%

Q3 10%

Q4 25%

0

50

100

150

200

250

0 25 50 75 100

Rai

nfa

ll In

ten

sity

(mm

/h)

Duration (%)

Q1 20%

Q2 40%

Q3 25%

Q4 15%

C

0

50

100

150

200

250

0 25 50 75 100

Rai

nfa

ll In

ten

sity

(mm

/h)

Duration (%)

D Q1 21%

Q2 40%

Q3 18%

Q4 21%

0

50

100

150

200

250

0 25 50 75 100

Rai

nfa

ll In

ten

sity

(mm

/h)

Duration (%)

E Q4 18%

Q3 35%

Q2 24%

Q1 24%

0

50

100

150

200

250

0 25 50 75 100

Rai

nfa

ll In

ten

sity

(mm

/h)

Duration (%)

F Q4 10%

Q3 25%

Q2 35%

Q1 30%

74


75

4.4. Climatic drivers and temporal analysis of erosive events

The eight weather systems prevailing over Mauritius as identified by Fowdur et al.

(2014) (see Table 3-3) were each given representative symbols which were applied to the

120 events. These symbols were utilised in an attempt to classify the events into the

appropriate weather systems from synoptic data available from the Mauritius Meteorological

Services Monthly Bulletins of Climatological Summaries, Mauritius Meteorological Services

website (MMS, 2014a) as well as the Météo-France Réunion (Météo-France, 2015) website.

Although the lack of synoptic data at the event scale was a limitation to organising the events

into the appropriate weather systems from the available data, an attempt has been made to

classify the events accordingly.

More than half 63 (53%) of the total events analysed for this project are related to

cyclonic activity within the vicinity of the island. Only 5 (4%) of the total events are related to

non-cyclonic events, and have been identified as frontal mesoscale rainfall caused by cold

fronts (MMS, 2008). Due to the lack of available synoptic data, 53 (44%) of the total events

have unknown weather systems. During the study period, a few tropical cyclones were within

the vicinity of the island. Upon considering the dates that each of the 120 events occurred, a

number of dates coincide with rainfall caused by cyclonic activity occurring around the island.

Numerous cyclonic events occurred concurrently at all six stations. The most important dates

during which concurrent events occurred include; 20-26 March 2005 and 02-04 March 2006.

The period from 22 to 26 March 2005 is remarkable as it coincides with cyclone

Hennie that was classed as a Severe Tropical Depression (MMS, 2014c). Noticeable pre-

cyclonic rainfall also occurred at all six automated weather stations from 18 to 21 March

2005 (Météo-France., 2015). Hence, cyclone Hennie represents rainfall from multiple events

at all of the automated weather stations. As a result cyclone Hennie represents at least two

of the events at each of the six automated weather stations. While the rainfall from events

associated with Cyclone Hennie occur on the same day, the events rank differently at each

station based on the ranking of the total kinetic energy content generated of the respective

event. At the inland station of Arnaud, rainfall from cyclone Hennie corresponds to three of

top twenty events. At the coastal stations of Albion and Beaux Songes rainfall from cyclone

Hennie is event no. 9 and event no. 7 respectively. The rainfall depth, duration and erosivity

for the event recorded at the Albion station is much higher than the rainfall depth, duration

and erosivity at Beaux Songes. Rainfall from cyclone Hennie is represented by event no. 1

at Arnaud, Monbois and Grand Bassin. Despite the station at Grand Bassin has the highest

erosivity of these three stations, the station at Arnaud has the highest storm duration, depth


76

and Total Kinetic energy. Even though rainfall from event no. 3 at Trou aux Cerfs represents

cyclone Hennie it has higher rainfall erosivity than the rainfall from the event at Monbois,

where cyclonic Hennie is no. 1.

The start time of rainfall from Cyclone Hennie is also variable. Rainfall is first

recorded at Arnaud at (08:48) on the 22 March 2005, then 36 minutes later at Grand Bassin,

followed by Monbois a further 4 hours and 24 minutes later, then 1 hour later it is recorded

at Trou aux Cerfs, then a further 3 hours and 42 minutes later at Albion. Finally the rainfall

from the event commences at Beaux Songes 12 minutes after Albion. Rainfall from this

cyclonic event ends on the 24 March 2005 at both low altitude stations but continues until

the 25 March 2005 at all the other automated weather stations.

Another important period is 02 March 2006 to 04 March 2006 which coincides with

Cyclone Diwa (MMS, 2014c). Even though the rainfall from Diwa represents an event at all

six stations, the events rank differently (due to kinetic energy content). Cyclone Diwa

represents event no. 3 at Albion, whilst it is event no. 4 at both Monbois and Trou aux Cerfs

and at Beaux Songes it corresponds to event no. 5. At Arnaud it is event no. 2 and event

no. 8 at the Grand Bassin rainfall station. The rainfall received from Cyclone Diwa started a

day earlier at Arnaud and Trou aux Cerfs than at the four other automated weather stations.

The rainfall from the events at Arnaud and Trou aux Cerfs commenced within an hour of

each other, 18:24 at Arnaud and 17:24 at Trou aux Cerfs. Four of the six events associated

with rainfall from Cyclone Diwa extended one day past the date on which the tropical

cyclone officially ended. The event at Beaux Songes is the shortest, and only occurs from

03 March 2006 (22:12) to 04 March 2006 (20:42).

Even though the two concurrent events discussed above represent cyclonic events

some of the other concurrent events coincide with non-cyclonic rainfall, namely cold fronts,

sea breezes induced rainfall and thunderstorms. Frontal mesoscale rainfall was caused by

two cold fronts crossing the island on the 16 and 19 of May 2008 (MMS, 2008). These two

cold fronts were followed by easterly waves causing a very unstable atmosphere (MMS,

2008). These cold fronts caused heavy rain and thunderstorms across the entire island

(MMS, 2008). Despite, this cold front bringing widespread rainfall to the island- it is only

recorded as erosive events at the automated weather stations at Arnaud and Grand Bassin.

Though frontal mesoscale rainfall from this cold front represents event no. 3 at both Arnaud

and Grand Bassin, the erosivity of this event is noticeably higher at Arnaud than it is at

Grand Bassin. Pre-cold frontal uplift was recorded on the 14 May 2008 at the coastal station

of Albion (event no. 17) (MMS, 2008).


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Another noteworthy event occurred at Albion on the 8 March 2004 (Nel et al., 2012).

This is represented by event no. 8. During the 90 minute rainfall event, 59 mm of rainfall was

received and no rainfall was recorded at any of the other rainfall stations. Hence, it is

assumed that it was non-cyclonic in nature and was potentially due to a localised convective

rainfall event associated with thunderstorms at a storm scale.

The seasonal distribution of the erosive events is noteworthy. Mauritius has two

predominant annual weather seasons; a warm, wet summer from November to April and a

cool dry winter from May to October (Padya, 1989). Therefore, the seasonal distribution of

the 120 events was done according to this classification. Events occurring during the

summer months account for 102 (85%) of all 120 events, while those occurring during the

winter months only account for 18 (15%). The total rainfall depth (mm) received from all 120

events was 14045.2 mm, and 81% was received during the summer months and 19% was

received during the winter months of May to October. Granted the automated weather station

at Albion had the highest number of events during the winter months (20%), this winter

rainfall only accounted for 24% of the rainfall received at this station. Rainfall received at

Arnaud during the summer months is 92% of the total rainfall received at this station. At

Monbois, 87% of the rainfall received at this station was during the summer months. Trou

aux Cerfs (the station with the highest altitude) received 67% of the rainfall during the

summer months and 33% during the winter months. Event no. 1 and 2 at Trou aux Cerfs

occur during the winter months. This event at Trou aux Cerfs (event no.1) is responsible for

26% of the total KE and 49% of the rainfall erosivity received at this station, hence this event

has the potential to be deemed as highly erosive. It is highly likely that cold fronts were

responsible for these events as the passage of cold fronts throughout the winter months is

generally associated with an increase in convective activity and subsequent rainfall over

Mauritius, particularly in areas with distinct orographic characteristics (Padya, 1989) such as

those surrounding the automated weather station at Trou aux Cerfs. The annual distribution

of the events are substantial, 108 (90%) of 120 of the events occur during the first half of the

year from January to June, with January to March receiving the vast majority of the events

(87 of 108 events). In contrast, only 12 of the 120 events occurred during the second half of

the year.

In summary, Chapter 4 has presented the results of the objectives set out following

the methodology discussed in the previous chapter. The most important findings and

observations have been highlighted and will be discussed in the next chapter, Chapter 5.

Although, all the events exhibited temporal variability in rainfall depth and rainfall intensity,


78

some common intra-storm characteristics are present amongst all the events. In all cases,

the influence of the orography on the spatial and temporal variation of the events is apparent.

Events were not restricted to tropical cyclones, but also included other weather systems such

as cold fronts, sea breezes induced rainfall and thunderstorms. The intra-storm attributes of

the events have potential implications on soil erosion risk. These will be discussed in depth in

the following chapter.


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Chapter 5 : Discussion

The tropical environment of Mauritius is attributed to its size, location, topography,

local island-scale weather systems and maritime climate. This consequently allowed the

intra-storm investigation of erosive events (based on the ‘total kinetic energy generated’) to

be contextualised within a tropical island environment. Six automated weather stations

provided the opportunity to investigate how elevation can influence the intra-storm attributes

of rainfall parameters. Although no other weather station data were available at the time of

the study, the automated weather stations utilised for this project represent rainfall totals at

two geographical extremes of the island (low and high totals). Considering the results from

the previous chapter, this chapter discusses the identification of the erosive events from the

data provided by the Mauritius Meteorological Services (MMS), followed by the role of

Mauritius’ physiography on spatial and temporal differences to the within-storm distribution of

rainfall depth, extreme rainfall intensity and cumulative kinetic energies. Then the influence of

elevation, associated with orographic lifting, has on the erosive events at the geographical

extremes of the island will be discussed. The chapter concludes in a discussion of the

potential implications of the erosive events on soil erosion risk.

5.1. Identification of the erosive events

Only one other study (Nel, 2007) has identified and analysed intra-storm attributes of

rainfall parameters. The current study has attempted to analysis double the number of

erosive events utilised in the initial study by Nel (2007) as well as increased the time period

(duration) in order to provide a greater intra-storm analysis in an attempt to include more

weather event types. A climatological analysis of the erosive events, not done by Nel (2007),

was also undertaken in this study. An attempt was made to classify the erosive events into

the appropriate synoptic weather systems. However, the absence of synoptic descriptions of

the daily weather patterns and the lack of synoptic data at the event scale was a limitation to

organising the events. However, some assumptions were made using the information

available from the Mauritius Meteorological Services Monthly Bulletins of Climatological

Summaries, Mauritius Meteorological Services website (MMS, 2014a) as well as the Météo-

France Réunion (Météo-France, 2015) website.

While a few months of the data acquired from the MMS were missing or incomplete,

the synchronicity of the data allowed for the direct inter-station comparisons. Subsequently,


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the events utilised in this intra-storm analysis may possibly not have been the actual top

twenty events at each automated weather station, but were the top measured events at each

station. These events were also not defined in the context of extreme events, they were

simply taken as the twenty events with the highest total kinetic energy producing events from

an erosivity perspective at the six automated weather stations.

5.2. Spatial and temporal variations of erosive events

Temporal and spatial variability of erosive events and the associated rainfall erosivity

is important in tropical areas, as rainfall erosivity is affected by storm type and varies with

topography and altitude (Salako, 2007). On Mauritius, spatial and temporal variability is

evident in the characteristics of the erosive rainfall recorded at the stations. Although

temporal variability with regard to rainfall depth is demonstrated by the 120 events, a

common characteristic amongst all the events is that more than 80% of the rainfall generated

is within the first 2500 minutes of the event. Therefore, visible tapering off of the storm

duration occurs after 2500 minutes at the majority of the stations analysed for this project.

However, the events at Grand Bassin are the exception, where despite noticeably longer

storms occurring, 80% of the cumulative rainfall is received within the first 2500 minutes from

the onset on the event. The location of this rainfall station, on the southern, leeward side on

the island could have a significant impact on the duration of the erosive events.

Mauritius experiences two distinct seasons, a rainy summer season from November

to April, and drier winter season from May to October (Padya, 1989; Nigel & Rughooputh;

2010a; Nel et al., 2012; Staub et al., 2014). The seasonality is predominately due to the

migration of the Inter Tropical Convergence Zone (ITCZ) (Fowdur et al., 2014). The ITCZ is

closest to Mauritius from January to March, subsequently warm season depressions affect

the island, resulting in high rainfall and markedly more erosive events. This is evident from

the 120 events, with 73% of the events occurring from January to March during the

southward migration of the ITCZ towards the subtropical latitudes and the occurrence of

tropical cyclones (Staub et al., 2014). During the winter months, the ITCZ is located north of

the equator causing anticyclonic conditions with the interruption of the occasional cold front

that are associated with an increase in convective activity over the island (Staub et al., 2014).

While only 15% of the 120 events occurred during the winter, large percentages of winter

rainfall events on Mauritius have been deemed as erosive and is rainfall associated with non-

tropical cyclones that posed substantial soil erosion risk (Nigel & Rughooputh, 2010b; Nel et

al., 2012).


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At the inland automated weather stations of Arnaud and Monbois, 92% and 87%

respectively, of the rainfall associated with the events is received during the summer months

when the erosion risk is most significant. At the automated weather station of Trou aux Cerfs,

67% of the erosive rainfall is received during the summer months, and 33% during the winter

months, which is similar to findings made by Anderson (2012); Mongwa (2012) and Nel et al.

(2012). Therefore the seasonal distribution of erosive rainfall received at this station is better

than at the other automated weather stations, indicating that this station receives more

erosive rainfall during winter than any other station. Event no. 1 and no. 2 at Trou aux Cerfs

are assumed to be cold fronts. The passage of cold fronts throughout the winter months is

generally associated with an increase in convective activity and subsequent rainfall over

Mauritius, particularly in areas with distinct orographic characteristics (Padya, 1989) such as

those surrounding the automated weather station at Trou aux Cerfs. Event no. 1 at Trou aux

Cerfs is responsible for 49% of the rainfall erosivity received and accounts for the highest

total rainfall depth received from the 120 events. Thus, these two non-tropical cyclonic

events pose huge erosional risks at this station. Elevation is responsible for Trou aux Cerfs

receiving fewer total events than the other stations located on the central interior (Anderson,

2012; Nel et al., 2012).

Mauritius, similar to other tropical islands, has a noticeable spatial difference in

rainfall across the island (Bender et al., 1985; Barcelo et al., 1997; Yen & Chen, 2000;

Fowdur et al., 2006). Flatter, lower coastal areas generally remain drier than the central

plateau throughout the year, except in January and February when the mean annual rainfall

is high even on the west coast, or the leeward side of the island due to the southeasterly

tradewinds (Staub et al., 2014). Rainfall variability of the erosive events is comparable to the

mean annual rainfall which varies longitudinally across the island from 1400 mm in the

eastern coastal lowlands, to 4000 mm on the uplands and 800 mm along the western coastal

lowlands (Rughooputh, 1997; Dhurmea et al., 2006; WRU, 2007). This spatial differentiation

is evident in the characteristics of the erosive rainfall at the stations, with a noticeable

increase in all intra-storm variables measured at the coastal versus the interior stations (Nel

et al., 2012). Rainfall received from the events at the west coast station (Albion and Beaux

Songes) is noticeably less than the erosive rainfall of the uplands station (Arnaud, Monbois,

Grand Bassin and Trou aux Cerfs).


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5.3. The influence of elevation on the erosive events at the respective

stations

Orography is the influence of mountain topography on subaerial conditions and the

most striking orographic effect is on the distribution of rainfall across an area (Terry &

Wotling, 2011). Places characterised by complex topography, such as Mauritius, typically

receive rainstorms because topographic forms drawing out atmospheric moisture through

orographic precipitation mechanisms (Barstad & Smith, 2004). The best known relationship

of the orographic effect- rainfall increases with elevation (Prudhomme & Reed, 1998).

Mauritius’ rainfall gradient is due the orographic effects of the South Easterly trade winds

interacting with the south-eastern mountain range and the central uplands (Fowdur et al.,

2006). Therefore, distribution of rainfall is highly dependent on an island’s topography (Hoyos

et al., 2005; Dhurmea et al., 2009; Senapathi et al., 2010). However, the results from this

project contradict the general relationship that rainfall increases with elevation and better

align themselves with findings made by Hoyos et al. (2005), who found that in the tropical

mountainous areas of the Colombian Andes, seasonal rainfall increases to a certain

elevation then decreases. Even though the change in elevation on Mauritius is less than the

change in elevation noted by Hoyos et al. (2005), the events at Trou aux Cerfs (614m.a.s.l;

the station with the highest altitude), had a lower total rainfall depth than the other stations on

the elevated central plateau, namely Arnaud (576m.a.s.l), Monbois (590m.a.s.l) and Grand

Bassin (605m.a.s.l). Hence, the importance of elevation, surrounding geographic features

and microclimates on the rainfall distribution and characteristics that an area experiences.

Owing to variations in diurnal exposure, elevation and distance from the sea, a

succession of island scale climatic regions or microclimates exist (Nigel & Rughooputh,

2010a). Twenty seven microclimates have been identified by Padya (1989), despite the small

size of the island. The substantial variations in the microclimatic characteristics of Mauritius

are derived from the climate (Rughooputh, 1997). Subsequently, the geographic features and

microclimates of the landscape surrounding of the weather stations influence the rainfall

distribution, erosivity and rainfall intensity of the erosive events. Even with both Albion and

Beaux Songes being situated on the west coast, Albion (12 m.a.s.l) receives more rainfall

than Beaux Songes (225 m.a.s.l) thus highlighting the influence which the rain-shadow and

surrounding geographic features has on Beaux Songes and the influence the Indian ocean

potentially has on rainfall received at Albion.

The westward or leeward orientation of the orographic barrier is also important when

associating rainfall and elevation (Staub et al., 2014). The diverse microclimates on the


83

island could be used to explain and interpret the intra-storm differences between the stations

on the interior of the island. Despite the automated weather stations of Arnaud, Monbois,

Grand Bassin and Trou aux Cerfs are all located on the elevated interior, the distinction

should be made to exactly where on the elevated interior each station is found. Arnaud and

Trou aux Cerfs stations are located within the same microclimatic zone on the leeward side

of the central plateau and the Monbois station is located on the westward side of the central

plateau. The southern and east coasts of Mauritius received higher rainfall than the west and

north coast due to the prevailing southeasterly trade winds (Fowdur et al., 2014; Staub et al.,

2014).

As the Grand Bassin station is located on the boundary between the central plateau

and the south uplands, it is speculated that the uplands located near this station could affect

the development of erosive events, and in turn affect the local climate around the station

(Anderson, 2012). Influences of the southeasterly trade winds could be more apparent at

Grand Bassin due to the geographic features and microclimate of the landscape surrounding

the weather station. The unique location of Grand Bassin is responsible for this station

receiving the highest rainfall depth of all six stations (Anderson, 2012). Thus results indicate

that the intra-storm attributes of rainfall events are strongly dependent on the geographic

features within the immediate surroundings of the weather stations, and distance between

weather stations did not always lead to predictable differences in intra-storm attributes

Rainfall intensity and erosivity increases with elevation; with the station at Trou aux

Cerfs recording the both the highest maximum intensity (I30) and the highest rainfall erosivity.

This is dissimilar to the Nel et al. (2007) study in the Drakensberg that covered an altitudinal

range (1060m to 3165 m.a.s.l), where the lower altitude stations recorded the higher

maximum intensities and erosivities than those at higher altitudes. Differences between the

temperate (Drakensberg) and tropical (Mauritius) could be attributed to the differences in

dominant rainfall generating mechanisms occurring in each region and altitudinal range (∆H),

influencing the amount of precipitation received as well as the intensity and kinetic energy of

the rainfall. The main rainfall generating mechanism in most tropical region are

predominately convection and cyclonic, and as a result the tropics receive more rain at

higher intensities than the temperate regions whose rainfall is dominated by mid-latitude

cyclones and consequently larger EI30 values are observed in the tropics (Hoyos et al.,

2005). Since, the erosive power of the precipitation is accounted for by the rainfall-erosivity

factor R, the individual stations all experience different erosivity because of the combined

effect of magnitude, duration and intensity of each rainfall event (Bonilla & Vidal, 2011).

Hence, if all other parameters are kept constant, soil loss is directly proportional to the rainfall


84

erosivity (Wang et al., 2002) Trou aux Cerfs would consequently have the highest rates of

potential soil loss.

Regardless of knowing that rainfall erosivity of an event is influenced by the intra-

storm distribution of rainfall intensity, it is only recently that such variability has been

incorporated into soil-erosion modelling (Parsons & Stone, 2006). Rainfall intensity is

regarded as a fundamental control of interrill runoff and erosion because rainfall intensity can

have an effect on the size distribution of the detached sediment as well as the total amount

of detached sediment (Parsons & Stone, 2006). Since runoff is inversely related to infiltration

the effect of rainfall intensity on runoff can be understood through its effects on infiltrat ion

(Parsons & Stone, 2006). Timing of the peak rainfall depth and peak extreme intensity is

paramount to the infiltration as well as the potential runoff and erosion experienced at each

station.

Although the events display variability in both rainfall depth and rainfall intensity,

similarities in the timing of the peaks are present. In general, the peak rainfall depth

corresponds almost directly to the peak rainfall intensities experienced. Peak rainfall depth

and peak rainfall intensities are received during the first half of the event at both low altitude

stations (Albion and Beaux Songes) as well as Monbois on the windward side of the central

plateau. At the Grand Bassin and Arnaud automated weather stations the peak rainfall is

more variable, but it still corresponds to the peak intensities occurring during the first half of

the event. The automated weather station at Trou aux Cerfs is the exception, where the peak

rainfall and intensities are received during the middle part (Q2 and Q3) of the event. Events

peaking towards the end of the event could have detrimental effects on peak runoff rates,

potential soil loss and the overall erosivity. Elevation might be responsible for the majority of

the events at Trou aux Cerfs having their peak rainfalls and intensity during the second half

of the event. It should be emphasised that despite this, the erosive events show high

temporal variability with no event showing constant intensity over time. The timing of the

peak rainfall depth and peak intensities as well as the specific storm pattern have important

implications for the amount of soil loss and the size of the particle being eroded. Rainfall

intensity is a key measure of rainfall erosivity, for example a region with higher rainfall

intensities could have more or similar erosivity when related with a region with higher rainfall

amount and more frequent rainfall (Salako, 2008).

Erosive events peaking in intensity towards the end in conjunction with varying-

intensity events have the highest peak runoff rates and soil loss (Nel, 2007). The highest

potential soil erosion risk is anticipated to occur at Trou aux Cerfs as this station received the

most consistently high rainfall intensity of any station. Parsons & Stone (2006) emphasise


85

that events with higher rainfall intensity, are responsible for higher percentages of coarser

particles and a higher clay content being found in the eroded sediment because of the

breakdown of the cohesion between the particles. Under natural rainfall conditions the peak

sediment transportation coincides with the peaks in rainfall intensities (Moore, 1979).

Consequently, the erosivity of the erosive rainfall in Mauritius is strongly influenced by both

the timing of peak intensity and the specific event pattern.

A similar intra-storm study conducted in the Drakensberg by Nel (2007) found 70% of

the erosive events measured have intensities above the 25mm/h during the first 100minutes

of event duration and all the stations receive a high proportion of peak rainfall intensity within

the first half of the event, then the erosivity of erosive rainfall in the Drakensberg could be

moderated by the within-storm distribution of rainfall intensity. The Drakensberg study shows

that despite common tendencies of rainfall event structure between events and between

stations can be distinguished, the within-storm distribution indicated that no two events are

precisely similar and no two stations in this region show similar distribution of event patterns.

The study by Nel (2007) implies that the structure of erosive rain is both site and event

specific, and due to the many spatial disparities exist for intra-storm rainfall distribution which

exist between the small number of stations measure in this study, it is not possible to

extrapolate the study by Nel (2007) for the Drakensberg region as a whole. However, the

events experienced on Mauritius share some common characteristics between events and

stations. The intra-storm analysis suggests despite the spatial differentiation in the structure

and nature of the erosive rainfall, generalisations can be made regarding the potential

erosion experienced in the coastal and interior regions of the island.

5.4. The potential implication of the erosive events on soil erosion risk

Soil erosion risk, like rainfall, on Mauritius varies seasonally (Le Roux, 2005; Nigel &

Rughooputh, 2010b). Considerable soil erosion risk in summer is as a result of tropical

cyclones, thunderstorms and orographically-induced rainfall (Le Roux, 2005; Seerutten et al.,

2007). Just over half (52%) of the 120 events are related to cyclonic activity and posed high

erosion risk, as between 48% and 68% of the soil erosion on the island is associated with

cyclonic activity (Seerutten et al., 2007). Furthermore, the erosion risk maps by Nigel &

Rughooputh (2010b) show that all land cover and cultivation types on Mauritius are at risk of

severe erosion, particularly during intense cyclonic events during January and February.

While the events support these notions, it also highlights the importance of considering non-

tropical cyclones which were also shown to pose substantial erosion risk.


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The greatest risk of soil erosion on Mauritius typically occurs from December to April

each year, and is linked to the severe tropical cyclones and accompanying torrential rainfall

experienced during this period (Nigel & Rughooputh, 2010b; Nigel, 2011). Furthermore, the

lack of vegetation in December results in this month posing the highest erosion hazard (Nigel

& Rughooputh, 2010b). The humid and super-humid climates of the central interior (Arnaud,

Monbois, Grand Bassin and Trou aux Cerfs) are inclined to experience greater erosion than

the sub-humid coastal regions of the island (Albion and Beaux Songes) (Nigel, 2011). Soil

erosion risk then decreases to moderate levels during March to April and is very low during

May to October (Nigel, 2011).

Erosion risk patterns closely, and inversely, follow patterns of vegetation cover on the

island. Vegetation cover is at its greatest during March, following on from the peak rainfall

depth received during February, and consequently provides the most protection from erosion

during this time. As rainfall decreases from April to November so does the vegetation cover,

causing an increase in the erosion hazard of rainfall (Nigel & Rughooputh, 2010b).

Consequently, the potential soil erosion risk is highest during the earliest part of the wet

season (December) when the majority of the rainfall occurs and vegetation cover is at its

lowest and does not yet provide adequate protection to the soil (Morgan, 2005; Nigel &

Rughooputh, 2010b). The erosive events in this study follow the same seasonal rainfall

pattern, thus exacerbating the soil erosion when the erosion risk is at its highest.

On Mauritius, areas with flat terrain and lowly erodible soils, like those found at

Albion, have low erosion susceptibility (Nigel, 2011). Furthermore, areas with undulating

terrain, similar to the terrain at Beaux Songes, exhibit moderate erosion susceptibility.

Mountainous areas and valley sides, comparable to the terrain at Arnaud, Monbois, Grand

Bassin and Trou aux Cerfs have very high levels of erosion susceptibility. The central

elevated plateau, the mountain environment and the southern and eastern regions also have

the highest soil risk (Nigel & Rughooputh, 2010). Additionally these regions also correspond

to the areas with the highest annual rainfall. However, the land cover in the mountainous

regions and valley sides provides some protection against soil erosion, especially due to the

existence of forests and other natural dense vegetation (Nigel, 2011).

Natural vegetation cover is also strongly influenced by orographic effects, which

promote high levels of variability in rainfall, and result in stark differences in natural

vegetation between the wet windward and the drier leeward sides of the island (Terry &

Wotling, 2011). The sparse shrub-like natural vegetation in conjunction with the extensive

sugarcane cultivation in the drier, leeward, west coast region could result in the soils here

being more vulnerable to erosion than in the central region of the island which has denser


87

vegetation, known to cope with a higher rainfall intensity and therefore be more resistant to

erosion by direct rainfall (Le Roux, 2005).

On the contrary, the extensive sugarcane cultivation on the west coast could aid in

decreasing soil erosion, as the soil under the sugarcane is not disturbed during harvest and a

dense protective cover is provided within less than two months after regrowth or planting (Le

Roux et al., 2005). However, good land management is vital as the potential for soil erosion

increases substantially when the sugarcane’s canopy cover decreases during harvests

(Kremer, 2000). The importance of vegetation cover for reducing the erosion susceptibility of

soils is further emphasized by the high erosivity values and erosion susceptibility of the upper

catchment areas. These regions have erosivity values four to five times higher than coastal

areas and results in a much higher erosion risk, particularly on any poorly vegetated, steep

slopes (Le Roux, 2005) and vegetation therefore plays a critical role in reducing the erosion

susceptibility of the soils and erosion risk in the mountainous areas on the central plateau.

Daily, monthly or annual rainfall totals are used as an indication of rainfall erosivity in

all soil risk models and assessments on Mauritius (Kremer, 2000; Le Roux et al., 2005; Nigel

& Rughooputh, 2010a, b). But previous studies (Le Roux, 2005; Anderson, 2012; Mongwa,

2012) have shown that the erosive events generate a large amount of the erosivity and

therefore simply using the annual or monthly rainfall measurements could potentially

underestimate the erosion risk at an island scale. Therefore, in order to increase the

effectiveness of soil risk assessments in a tropical maritime environment, such as Mauritius,

the temporal time scale of the data used should be adjusted to adapt with the storm to

synoptic scale systems that dominate the risk for soil erosion (Mongwa, 2012).

There is general consensus that dramatic changes in the hydrological cycle stemming

from global climatic changes will cause more erosive events in the future (Nearing et al.,

2004). While this is an important consideration warranting further attention, the limited

duration (5 years) of the data used in this study prevented such long-term predictions

regarding the intra-storm attributes of erosive events from being undertaken. Findings from

Senapathi et al. (2010) do however indicate that rainfall frequency, at an island scale, is

expected to increase and subsequently alter the temporal distribution and intensity of the

rainfall and culminate in potentially increasing the erosivity of Mauritius (MMS, 2014a).

Future increases in rainfall intensity could therefore be associated with higher erosivity

values across all stations on the island (Anderson, 2012). Increased rainfall frequency and

erosivity is particularly relevant from a meteorological and an agricultural perspective, as

climate change will certainly have implications for the agricultural practices and ecological

systems in Mauritius (Senapathi et al., 2010). The effects of climate change on the intra-


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storm attributes of erosive rainfall on Mauritius should therefore be investigated in future

work as it potentially poses substantial changes to soil erosion risk. Soil erosion risk

assessments and modelling will become critical to understanding the current soil erosion

risks as well as future trends in establishing suitable and dynamic soil conservation

management plans and strategies.

Chapter 5 presented a discussion based on the results that were found in Chapter 4.

The importance of the geographic location of the stations and climatic zones of the

differences between the intra-storms attributes experienced in different regions was

addressed. Additionally, the orographic influence of the elevated central plateau on rainfall

and erosivity was highlighted. Seasonality of the erosive events and vegetation cover was

found to pose a substantial threat to soil erosion risk. The project concludes in the following

chapter by highlighting the most significant findings made during this study.


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Chapter 6 : Conclusion

The tropical volcanic island of Mauritius has a distinct elevated central plateau which

rises more than 550 m.a.s.l and interacts with the island’s predominantly tropical weather

systems and south-easterly trade winds (Fowdur et al., 2014; Staub et al., 2014). This study

made use of two weather stations on the west coast (Albion and Beaux Songes) and four

stations on the Central Plateau (Arnaud and Trou aux Cerfs on the leeward side, Monbois on

the windward side and Grand Bassin on the southern side) that provided high resolution

rainfall data which enabled the first intra-storm analysis on the island. Although previous

studies have been undertaken on storm kinetic energy, erosivity and soil erosion risk of

rainfall in Mauritius (Le Roux, 2005; Nigel & Rughooputh, 2010a, b; Nel et al., 2012; 2013),

very little is known regarding the intra-storm attributes and general climatology of rainfall

parameters in tropical island environments. This project therefore aimed to present findings

on the intra-storm attributes and climatological characteristics of erosive events at two

physiographic extremes on Mauritius: the west coast and the elevated central plateau. The

within-storm distribution of rainfall depth, extreme rainfall intensity, cumulative kinetic

energies and general climatological characteristics of the dry west coast were compared to

those of the higher altitude stations situated in the central region which received the highest

rainfall. A number of conclusions are presented on the intra-storm attributes of the erosive

events on a tropical island with an elevated central plateau:

6.1. Seasonality and spatial distribution of the erosive events

Rainfall seasonality on Mauritius is predominately owing to the migration of the Inter

Tropical Convergence Zone (ITCZ) (Fowdur et al., 2014), which results in noticeably higher

rainfall and more erosive events during the summer months when the ITCZ is closest to the

island (Fowdur et al., 2014; Staub et al., 2014). Although this study found that only a small

number of events occurred during the winter months, a large proportion of these have been

deemed erosive and were associated with non-tropical cyclonic events (Nel et al., 2012). The

events were, therefore, not restricted to tropical cyclones and this study has highlighted the

need to consider other weather systems such as frontal mesoscale rainfall from cold fronts

(MMS, 2008) which also posed substantial soil erosion risk.

Mauritius, like other tropical islands, shows noticeable spatial variation in rainfall

(Bender et al., 1985; Barcelo et al., 1997; Yen & Chen, 2000; Fowdur et al., 2006) which is


90

also evident when considering the intra-storm characteristics of erosive events at the

automated weather stations across the island. This study found a noticeable increase in all

intra-storm variables measured at the coastal weather stations compared to those in the

interior. The amount of rainfall during events at the west coast stations (Albion and Beaux

Songes) was noticeably less compared to that of uplands stations (e.g. Arnaud, Monbois,

Grand Bassin and Trou aux Cerfs). Rainfall intensity and erosivity increased with elevation

and spatial differences in erosivity were related to the total number of erosive events along

with the frequency of these events at each station. Differences in erosivity between the coast

and the elevated interior are not only related to absolute rainfall depth, but also the nature of

the events.

6.2. Elevation and the erosive events

Elevation is a key factor influencing the intra-storm attributes of the erosive events on

Mauritius. This study confirmed findings from other studies which highlight the importance of

the elevated interior as a topographical feature primarily responsible for the high rainfall

gradient present across the island (Anderson, 2012; Mongwa, 2012; Nel et al., 2012).

Differences in the occurrence of erosive events across the island were documented by

Anderson (2012); Mongwa (2012); Nel et al. (2012) which reinforced the idea of a ‘rain-

shadow’ effect created by the interaction between the elevated central interior and the south-

easterly tradewinds with other local weather systems (Fowdur et al., 2014; Staub et al.,

2014). Definite regions were apparent across the island which displayed marked differences

in rainfall intensity, intra-storm attributes, erosivity and total kinetic content. The west coast

region experienced lower rainfall depth, intensity, erosivity and total kinetic content compared

to the elevated central plateau region. This again highlights the influence of the elevated

interior on the general rainfall characteristics of the events.

The analysis of intra-storm attributes revealed that the assumption of a positive

association between rainfall and altitude is inadequate, as mean rainfall depth and kinetic

energy content produced by the events measured during this study did not simply increase

with increasing altitude. Rainfall received from the events at the automated weather stations

indicated that rainfall increased with elevation up to a point and then decreases. This study

has demonstrated that the spatial distribution of rainfall amount, intensity and erosivity stems

from complex interactions between the elevation, geographic features (i.e. the remnants of

the shield volcano and central plateau) and microclimates of the landscapes surrounding the


91

weather stations, and that simply considering the distance between automated weather

stations does not always lead to predictable differences in intra-storm attributes.

While the erosive events display variability in both rainfall depth and rainfall intensity,

similarities in the timing of the peaks are present. Peak rainfall depth almost always

corresponded to the peak rainfall intensities experienced, with the exception of Trou aux

Cerfs where the events tended to peak towards the end. Such behaviour could have an

increasing effect on peak runoff rates, potential soil loss and the overall erosivity of the

storms. Events generally showed high temporal variability as no event had a constant

intensity over time. The timing of the peak rainfall depth and peak intensities as well as the

individual event characteristics are all vital to consider and may perhaps have important

implications for the amount of soil loss and potential soil erosion experienced at any given

location on the island.

6.3. Potential erosion risk of the erosive events

Soil erosion risk on Mauritius varies seasonally (Le Roux, 2005; Nigel & Rughooputh,

2010b). The potential for soil erosion is greatest during the earliest part of the wet season

when the majority of the erosive events occur and vegetation cover does not yet provide

sufficient protection to the soil (Morgan, 2005; Nigel & Rughooputh, 2010b). Erosive rainfall

in combination with the amount, density and type of vegetation cover is therefore considered

to be responsible for determining the potential erosion risk over the entire island. Natural

vegetation on the western side of the island is sparse, shrub-like and grassy, which could

potentially make the soil more vulnerable to erosion from erosive rainfall. Conversely, areas

in the interior are considered less vulnerable to the effect of erosive rainfall, as natural

vegetation is denser and therefore provides greater protection to the soil during these erosive

events (Le Roux, 2005). However, the extensive sugarcane cultivation on the west coast

decreases potential soil erosion risk. Good land management is essential in this regard as

potential soil erosion increases greatly when the canopy cover of sugarcane decreases

during harvests (Kremer, 2000).

An area’s terrain and the erodibility of the soils greatly influence its erosion

susceptibility. Flat and undulating terrain, such as found at the west coast stations of Albion

and Beaux Songes, has a lower erosion susceptibility than mountainous areas and valley

sides, such as that around the stations on the elevated central plateau (Arnaud, Monbois,

Grand Bassin and Trou aux Cerfs). Despite the dense natural vegetation of the central


92

plateau acting to protect the soils against erosion, this region is actually considered to be

most at risk. This is as a result of the steep terrain and soil type causing a high level of

erosion susceptibility in addition to the large amount of erosive rainfall and high rainfall

erosivity received (Nigel, 2011). The intra-storm distribution of rainfall in this area also

worsens the situation as the rainfall generated from the erosive events has the ability to

generate high peak runoff rates in the latter parts of the storms thereby enhancing the

amount of soil loss experienced.

Despite the longstanding recognition that the rainfall erosivity of an event is

influenced by the intra-storm distribution of rainfall intensity, such variables have only

recently been incorporated into soil-erosion modelling (Parsons & Stone, 2006). Daily,

monthly or annual rainfall totals are frequently used as an indication of rainfall erosivity in all

soil risk models and assessments on Mauritius (Kremer, 2000; Le Roux et al., 2005; Nigel &

Rughooputh, 2010a, b). But previous studies (Le Roux, 2005; Anderson, 2012; Mongwa,

2012) have shown that event scale rainfall generate a large amount of the erosivity

experienced and therefore simply using annual or monthly rainfall measurements may

underestimate the erosion risk at an island scale. Therefore, in order to increase the

effectiveness of soil risk assessments in tropical maritime environments, such as Mauritius,

the temporal scale of the data used should be adjusted to adapt with the storm (event scale)

to synoptic scale systems that dominate the risk for soil erosion (Mongwa, 2012). The

highest possible resolution of rainfall data should also be utilised to ensure more accurate

soil risk assessments and greatly assist Mauritian authorities in defining and prioritising key

areas for soil conservation measures, promote better land use management, agricultural

practices and conservation planning to maximise the limited land resources on the island.

Erosion risk models and assessments generally assume that erosive rain falls at a

constant intensity yet the intra-storm analysis of this study demonstrates that constant

intensity erosive events are non-existent on Mauritius and presumably not in any other

tropical island environment. This project has therefore shown that intra-storm rainfall

characteristics have important implications for soil erosion risk, especially in the elevated

central plateau of Mauritius, and that a certain complexity is evident regarding the

characteristics of the erosive events measured at specific stations.

6.4. Research needs and recommendations

Although the erosive events on Mauritius share common characteristics between

events and stations, the within-storm distribution indicated that no two events are identical

and no two stations show comparable rainfall event pattern distributions. However, the intra-


93

storm analysis of the erosive events suggests despite the spatial differentiation in the

structure and nature of the erosive rainfall generalisations can be made regarding the

potential erosion experienced in the coastal and interior regions of the island. Furthermore,

the inclusion of more automated weather stations is warranted as this will provide a better

representation of rainfall characteristics across the island. Researchers are encouraged to

determine the possible erosional relationships between different event structures and

synoptic conditions on the island. Should more data become available at the sufficient

synoptic scale, it is suggested that erosive events with similar synoptic conditions are

grouped and the intra-storm attributes associated of each synoptic condition be established.

This will aid greatly in determining the potential erosional impact associated with each

synoptic condition relative to its intra-storm attributes and provide further the accuracy in

erosion risk assessments on the island of Mauritius. Lastly, the findings from this study are

directly applicable to the well-known and frequently applied (R)USLE model. However, the

results, specifically the within-storm distribution of rainfall depth, extreme rainfall intensity and

general climatological characteristics of the erosive events, found in this study also have the

potential to improve not only the future (R)USLE erosion models, but also other more

sophisticated non-(R)USLE modelling efforts, including modelling the sediment yield similar

the advanced event-based models described by van Rompaey et al. (2005) including the

WEPP (Nearing et al. 1989), LISEM (De Roo, 1996), EUROSEM (Morgan et al., 1998),

EROSION-3D (Schmidt et al., 1999). Spatially-distributed sediment delivery models that

assess the sediment yield usually have higher data demands, especially with regards to

rainfall and the description thereof to ensure the accurate estimations of sediment yield.


94

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